diff --git a/.cursor/rules/docs.mdc b/.cursor/rules/docs.mdc
new file mode 100644
index 0000000..c4a849e
--- /dev/null
+++ b/.cursor/rules/docs.mdc
@@ -0,0 +1,13 @@
+---
+description: "Documentation conventions"
+globs:
+  - "docs/**/*.md"
+  - "mkdocs.yaml"
+alwaysApply: false
+---
+
+# Documentation Rules
+- Write docs in Markdown with clear headings and code examples.
+- Update `mkdocs.yaml` navigation when adding new docs.
+- Keep `README.md` concise, and move long explanations to `/docs`.
+- Use Python code fences (```python … ```) in examples.
diff --git a/.cursor/rules/pydantic-usage.mdc b/.cursor/rules/pydantic-usage.mdc
new file mode 100644
index 0000000..8e89a27
--- /dev/null
+++ b/.cursor/rules/pydantic-usage.mdc
@@ -0,0 +1,13 @@
+---
+description: "Conventions for using Pydantic models and settings"
+globs:
+  - "**/*.py"
+alwaysApply: false
+---
+
+# Pydantic Rules
+- Use `pydantic.BaseModel` for request/response schemas.
+- For configuration, use `pydantic_settings.BaseSettings` (v2).
+- Always provide default values or use `...` for required fields.
+- Use model validators (`@field_validator`, `@model_validator`) instead of custom init logic.
+- Keep models small and focused on one purpose.
diff --git a/.cursor/rules/python-run.mdc b/.cursor/rules/python-run.mdc
new file mode 100644
index 0000000..035c5e1
--- /dev/null
+++ b/.cursor/rules/python-run.mdc
@@ -0,0 +1,12 @@
+---
+description: "Use `uv run` for running Python code instead of raw python3"
+globs:
+  - "**/*.py"
+alwaysApply: true
+---
+
+# Running Python code
+- **Do not** run `python3 -c` directly.
+- Always invoke Python code using `uv run python <script>` or `uv run <module>` when executing project scripts.
+- Use the `uv` environment so that dependencies from `uv.lock` are respected.
+- This applies to both interactive code execution and testing.
diff --git a/.cursor/rules/python-style.mdc b/.cursor/rules/python-style.mdc
new file mode 100644
index 0000000..4e12173
--- /dev/null
+++ b/.cursor/rules/python-style.mdc
@@ -0,0 +1,15 @@
+---
+description: "Follow project style guidelines for Python code"
+globs:
+  - "**/*.py"
+alwaysApply: true
+---
+
+# Python Coding Rules
+- Use python 3.12 conventions: use `type | None` instead of `Optional` etc
+- Use type hints everywhere (PEP 484).
+- Use snake_case for functions and variables, PascalCase for classes.
+- Avoid unused imports.
+- Use `async`/`await` where appropriate.
+- Always keep functions small and composable.
+- Avoid using hasattr at all costs, it's not safe
\ No newline at end of file
diff --git a/.cursor/rules/tests.mdc b/.cursor/rules/tests.mdc
new file mode 100644
index 0000000..cafea4a
--- /dev/null
+++ b/.cursor/rules/tests.mdc
@@ -0,0 +1,16 @@
+---
+description: "Testing guidelines"
+globs:
+  - "test/**/*.py"
+alwaysApply: false
+---
+
+# Testing Guidelines
+- Use `pytest` with descriptive test function names.
+- Prefer fixtures over global state.
+- For async code, use `pytest-asyncio`.
+- Ensure test coverage for:
+  - Pydantic model validation
+  - Env config via `BaseSettings`
+  - Error cases and edge cases
+- Run tests with `uv run pytest test`.
diff --git a/.cursor/rules/uv-deps.mdc b/.cursor/rules/uv-deps.mdc
new file mode 100644
index 0000000..2a3f24e
--- /dev/null
+++ b/.cursor/rules/uv-deps.mdc
@@ -0,0 +1,11 @@
+---
+description: "Dependency management with uv"
+globs:
+  - "pyproject.toml"
+  - "uv.lock"
+alwaysApply: false
+---
+
+# Dependency Management
+- Pin versions in `pyproject.toml` for reproducibility.
+- Run `uv pip check` to validate the dependency tree.
diff --git a/.github/workflows/build-docs.yml b/.github/workflows/build-docs.yml
new file mode 100644
index 0000000..f894a78
--- /dev/null
+++ b/.github/workflows/build-docs.yml
@@ -0,0 +1,54 @@
+name: Build GitHub Pages
+on:
+    push:
+        branches:
+        -   main
+        paths:
+        -   'docs/**'
+        -   'mkdocs.yml'
+        -   'pelinker/**' # Check this path
+    workflow_dispatch:
+
+permissions:
+    contents: write
+    pages: write
+    id-token: write
+
+jobs:
+    build:
+        runs-on: ubuntu-latest
+        steps:
+        -   name: Checkout code
+            uses: actions/checkout@v5
+            with:
+                fetch-depth: 0
+
+        -   name: Set up Python
+            uses: actions/setup-python@v5
+            with:
+                python-version: '3.12'
+        -   name: Install uv
+            uses: astral-sh/setup-uv@v7
+            with:
+                version: "0.10.12"
+        -   name: Install dependencies
+            run: |
+                uv sync --extra docs
+        -   name: Build site
+            run: |
+                uv run mkdocs build --site-dir _site
+        -   name: Upload Pages artifact
+            uses: actions/upload-pages-artifact@v3
+            with:
+                path: _site
+
+    deploy:
+        needs: build
+        environment:
+            name: github-pages
+            url: ${{ steps.deployment.outputs.page_url }}
+        runs-on: ubuntu-latest
+        steps:
+        -   name: Deploy to GitHub Pages
+            id: deployment
+            uses: actions/deploy-pages@v4
diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml
new file mode 100644
index 0000000..661200c
--- /dev/null
+++ b/.github/workflows/pre-commit.yml
@@ -0,0 +1,20 @@
+name: pre-commit
+
+on:
+    pull_request:
+    push:
+        branches: [main]
+
+jobs:
+    pre-commit:
+        runs-on: ubuntu-latest
+        steps:
+        -   uses: actions/checkout@v5
+        -   name: Install uv
+            uses: astral-sh/setup-uv@v7
+            with:
+                version: "0.10.12"
+        -   name: Install dependencies (including dev)
+            run: uv sync --extra dev --no-install-project
+        -   name: Run pre-commit
+            run: uv run pre-commit run --all-files
diff --git a/.github/workflows/pypi-publish.yml b/.github/workflows/pypi-publish.yml
new file mode 100644
index 0000000..5e3b0cd
--- /dev/null
+++ b/.github/workflows/pypi-publish.yml
@@ -0,0 +1,35 @@
+name: Upload Python Package to PyPI
+
+on:
+    release:
+        types: [published]
+
+permissions:
+    contents: read
+
+jobs:
+    pypi-publish:
+        runs-on: ubuntu-latest
+        environment:
+            name: pypi
+            url: https://pypi.org/p/pelinker
+        permissions:
+            id-token: write
+
+        steps:
+        -   uses: actions/checkout@v5
+
+        -   name: Set up Python
+            uses: actions/setup-python@v5
+            with:
+                python-version: '3.12'
+        -   name: Install uv
+            uses: astral-sh/setup-uv@v7
+            with:
+                version: "0.10.12"
+        -   name: Build and publish
+            run: |
+                uv build
+                uv publish
+            env:
+                UV_PUBLISH_TOKEN: ${{ secrets.PYPI_API_TOKEN }}
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index cf66d5c..ad7839a 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -3,7 +3,7 @@ fail_fast: false
 repos:
   # Python linting and formatting
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.14.14
+    rev: v0.15.7
     hooks:
     -   id: ruff-check
         args: [--fix, --ignore, E722]
diff --git a/pelinker/analysis.py b/pelinker/analysis.py
index 94319f6..3e95dc9 100644
--- a/pelinker/analysis.py
+++ b/pelinker/analysis.py
@@ -1,230 +1,25 @@
+from __future__ import annotations
+
 import pathlib
+from collections.abc import Callable, Iterable, Mapping, Sequence
+from typing import Literal
 
 import numpy as np
 import pandas as pd
 import torch
 import hdbscan
-from pelinker.io import read_batches
-from pelinker.reporting import ClusteringReport
-from sklearn.metrics import confusion_matrix
-from scipy.optimize import linear_sum_assignment
-from torch.nn import functional as F
-from collections.abc import Mapping
-from typing import Iterable
+from pelinker.clustering_grid import (
+    aggregate_grid_metrics,
+    evaluate_cluster_size_grid,
+    solve_optimal_min_cluster_size_from_aggregated,
+)
+from pelinker.embedding_fusion import concat_mention_level_embedding_sources
+from pelinker.reporting import ClusteringHyperparameters, ClusteringReport
+from sklearn.metrics import adjusted_rand_score
 from numpy.random import RandomState
 
-from pelinker.transform import TransformConfig, get_umap_columns, transform_embeddings
-
-
-def evaluate_cluster_size_grid(
-    dfr2: pd.DataFrame,
-    umap_columns: list[str],
-    sizes: list[int],
-    max_pairs_per_cluster: int = 200_000,
-) -> pd.DataFrame:
-    """
-    Evaluate clustering metrics on a grid of min_cluster_size values.
-
-    Uses DBCV (Density-Based Clustering Validation) metric.
-
-    Args:
-        dfr2: DataFrame with UMAP-reduced embeddings
-        umap_columns: List of UMAP column names
-        sizes: List of min_cluster_size values to evaluate
-
-    Returns:
-        DataFrame with columns: min_cluster_size, icm, n_clusters, dbcv
-    """
-    # OPTIMIZATION: materialize UMAP features once as float32 to reduce repeated
-    # DataFrame slicing and keep HDBSCAN input memory footprint lower.
-    umap_values = dfr2[umap_columns].to_numpy(dtype=np.float32, copy=False)
-
-    metrics = []
-    for size in sizes:
-        clusterer = hdbscan.HDBSCAN(min_cluster_size=size, gen_min_span_tree=True)
-        labels = clusterer.fit_predict(umap_values)
-
-        ic = []
-        for ix in np.unique(labels):
-            if ix == -1:  # Skip noise points
-                continue
-            cluster_values = umap_values[labels == ix]
-            if len(cluster_values) < 2:
-                continue
-            tgroup = torch.from_numpy(cluster_values)
-            st = cosine_similarity_std(
-                tgroup, max_pairs=max_pairs_per_cluster, random_seed=13
-            )
-            ic += [float(st)]
-
-        icm = np.mean(ic) if ic else np.nan
-
-        # Compute DBCV score
-        unique_labels = set(labels)
-        n_clusters = len(unique_labels) - (1 if -1 in unique_labels else 0)
-
-        # DBCV is available as relative_validity_ attribute
-        if n_clusters >= 2 and hasattr(clusterer, "relative_validity_"):
-            dbcv = float(clusterer.relative_validity_)
-        else:
-            dbcv = np.nan
-
-        # Only record if we have at least one valid cluster
-        if n_clusters >= 1:
-            metrics += [(size, icm, n_clusters, dbcv)]
-
-    return pd.DataFrame(
-        metrics, columns=["min_cluster_size", "icm", "n_clusters", "dbcv"]
-    )
-
-
-def aggregate_grid_metrics(
-    all_metrics_dfs: list[pd.DataFrame],
-) -> pd.DataFrame:
-    """
-    Aggregate grid evaluation metrics across multiple samples.
-
-    Args:
-        all_metrics_dfs: List of metrics DataFrames from multiple samples
-
-    Returns:
-        DataFrame with columns:
-        - min_cluster_size
-        - dbcv_mean, dbcv_std, dbcv_count
-        - icm_mean, n_clusters_mean
-    """
-    if not all_metrics_dfs:
-        return pd.DataFrame()
-
-    combined = pd.concat(all_metrics_dfs, ignore_index=True)
-
-    aggregated = (
-        combined.groupby("min_cluster_size")
-        .agg(
-            {
-                "dbcv": ["mean", "std", "count"],
-                "icm": "mean",
-                "n_clusters": "mean",
-            }
-        )
-        .reset_index()
-    )
-
-    # Flatten column names
-    aggregated.columns = [
-        "min_cluster_size",
-        "dbcv_mean",
-        "dbcv_std",
-        "dbcv_count",
-        "icm_mean",
-        "n_clusters_mean",
-    ]
-
-    # Fill NaN std with 0 (single sample case)
-    aggregated["dbcv_std"] = aggregated["dbcv_std"].fillna(0.0)
-
-    return aggregated
-
-
-def find_optimal_from_grid(
-    aggregated_metrics: pd.DataFrame,
-    method: str = "mean",
-    uncertainty_penalty: float = 1.0,
-) -> tuple[int, float, float]:
-    """
-    Find optimal min_cluster_size from aggregated grid metrics.
-
-    Uses DBCV (Density-Based Clustering Validation) as the metric (maximize).
-
-    Args:
-        aggregated_metrics: DataFrame from aggregate_grid_metrics()
-        method: How to select optimum:
-            - "mean": Use mean DBCV score (default)
-            - "lower_bound": Use mean - uncertainty_penalty * std (conservative)
-            - "weighted": Weight by inverse variance (more samples = more weight)
-        uncertainty_penalty: Multiplier for std when using "lower_bound" method
-
-    Returns:
-        (best_size, best_score_mean, best_score_std) where score is DBCV
-    """
-    if len(aggregated_metrics) == 0:
-        raise ValueError("No aggregated metrics provided")
-
-    sizes = aggregated_metrics["min_cluster_size"].values
-    means = aggregated_metrics["dbcv_mean"].values
-    stds = aggregated_metrics["dbcv_std"].values
-
-    if method == "mean":
-        scores = means
-    elif method == "lower_bound":
-        # Conservative: prefer points with lower uncertainty
-        scores = means - uncertainty_penalty * stds
-    elif method == "weighted":
-        # Weight by inverse variance (more reliable = higher weight)
-        weights = 1.0 / (stds + 1e-8)  # Add small epsilon to avoid division by zero
-        # Normalize weights
-        weights = weights / weights.sum()
-        # Weighted average (but we still need to pick a discrete point)
-        # So we'll use weighted mean as score
-        scores = means * weights
-    else:
-        raise ValueError(f"Unknown method: {method}")
-
-    best_idx = np.argmax(scores)
-    best_size = int(sizes[best_idx])
-    best_score_mean = float(means[best_idx])
-    best_score_std = float(stds[best_idx])
-
-    return best_size, best_score_mean, best_score_std
-
-
-def cosine_similarity_std(
-    tensor: torch.Tensor, max_pairs: int = 200_000, random_seed: int = 13
-):
-    """
-    Calculate the standard deviation of pairwise cosine similarities
-    for a tensor of shape (n_b, dim_emb).
-
-    Args:
-        tensor: torch.Tensor of shape (n_b, dim_emb)
-
-    Returns:
-        torch.Tensor: scalar tensor containing the standard deviation
-    """
-
-    # OPTIMIZATION: use float32 to reduce memory pressure from intermediate tensors.
-    normalized = F.normalize(tensor.float(), p=2, dim=1)
-
-    n_points = normalized.size(0)
-    if n_points < 2:
-        return torch.tensor(float("nan"), dtype=normalized.dtype)
-
-    total_pairs = n_points * (n_points - 1) // 2
-
-    # For small clusters, exact computation is cheap and keeps original behavior.
-    if total_pairs <= max_pairs:
-        cos_sim_matrix = torch.mm(normalized, normalized.t())
-        triu_indices = torch.triu_indices(
-            cos_sim_matrix.size(0), cos_sim_matrix.size(1), offset=1
-        )
-        cos_similarities = cos_sim_matrix[triu_indices[0], triu_indices[1]]
-        return torch.std(cos_similarities)
-
-    # OPTIMIZATION: avoid O(n^2) similarity matrix for large clusters by sampling
-    # random pairs and estimating the std from sampled cosine similarities.
-    sample_size = min(max_pairs, total_pairs)
-    generator = torch.Generator(device=normalized.device)
-    generator.manual_seed(random_seed)
-
-    idx_i = torch.randint(
-        0, n_points, (sample_size,), generator=generator, device=normalized.device
-    )
-    idx_j = torch.randint(
-        0, n_points - 1, (sample_size,), generator=generator, device=normalized.device
-    )
-    idx_j = idx_j + (idx_j >= idx_i).long()  # ensure i != j
-    cos_similarities = (normalized[idx_i] * normalized[idx_j]).sum(dim=1)
-    return torch.std(cos_similarities)
+from pelinker.config import ClusteringOptimizationConfig, TransformConfig
+from pelinker.transform import get_umap_columns, transform_embeddings
 
 
 def get_word_frequencies_from_library(
@@ -339,17 +134,16 @@ def _measure_label_simplicity(
     }
 
 
-def compute_hungarian_accuracy(y_true: np.ndarray, y_pred: np.ndarray) -> float:
+def compute_adjusted_rand_index(y_true: np.ndarray, y_pred: np.ndarray) -> float:
     """
-    Compute clustering accuracy using Hungarian algorithm to optimally match
-    predicted cluster labels to true labels.
+    Compute clustering quality via adjusted Rand index (ARI).
 
     Args:
         y_true: True labels (e.g., property names)
         y_pred: Predicted cluster labels
 
     Returns:
-        Accuracy score between 0 and 1
+        ARI score.
     """
     # Filter out noise points (label -1) for accuracy computation
     valid_mask = y_pred != -1
@@ -362,148 +156,229 @@ def compute_hungarian_accuracy(y_true: np.ndarray, y_pred: np.ndarray) -> float:
     if len(y_true_valid) == 0:
         return 0.0
 
-    # Compute confusion matrix
-    cm = confusion_matrix(y_true_valid, y_pred_valid)
-
-    # Use Hungarian algorithm to find optimal assignment
-    # We negate the matrix because linear_sum_assignment minimizes cost
-    row_ind, col_ind = linear_sum_assignment(-cm)
-
-    # Compute accuracy based on optimal assignment
-    accuracy = cm[row_ind, col_ind].sum() / cm.sum()
+    ari = adjusted_rand_score(y_true_valid, y_pred_valid)
+    return float(ari)
 
-    return float(accuracy)
 
-
-def estimate_model_clustering(
-    file_path: pathlib.Path,
-    rns: RandomState,
+def estimate_clustering_from_frame(
+    dfr: pd.DataFrame,
     transform_config: TransformConfig,
+    optimization_config: ClusteringOptimizationConfig | None = None,
     *,
-    min_class_size: int = 20,
-    max_scale: int = 120,
-    frac: float = 0.1,
-    head: int | None = None,
-    batch_size: int = 1000,
     selected_labels: set[str] | None = None,
     all_metrics_dfs: list[pd.DataFrame] | None = None,
-    optimization_method: str = "mean",
-):
+    aggregation_level: Literal["mention", "property"] = "mention",
+) -> ClusteringReport | None:
     """
-    Estimate optimal cluster size for a single model/layer file.
-
-    Args:
-        file_path: Path to parquet file
-        rns: RandomState object for reproducible sampling
-        transform_config: TransformConfig instance specifying transformation parameters
-        min_class_size: Minimum class size for filtering
-        max_scale: Maximum value for grid evaluation of min_cluster_size (default: 120)
-        frac: Fraction of dataset to sample
-        head: Number of batches to take (None for all)
-        batch_size: Batch size for reading
-        selected_labels: Optional set of labels from selected labels KB to filter by
-        all_metrics_dfs: Optional list of metrics DataFrames from previous samples.
-                         If provided, will aggregate and find optimum from grid.
-                         If None, evaluates grid for this sample only.
-        optimization_method: Method for finding optimum ("mean", "lower_bound", "weighted")
+    Run clustering grid search and optional aggregation on a pre-built frame with an ``embed`` column.
 
-    Returns:
-        ClusteringReport or None: Report containing clustering results, or None if processing failed
+    ``aggregation_level="property"`` expects one row per distinct ``property`` (e.g. fused
+    KB vectors) and skips min-mention-per-property trimming used for mention-level corpora.
     """
 
-    # Simple check: if file doesn't exist (handles broken symlinks), skip it
-    if not file_path.exists():
-        return None
-    agg = []
-
-    try:
-        for i, batch in enumerate(
-            read_batches(file_path.as_posix(), batch_size=batch_size)
-        ):
-            sample = batch.sample(frac=frac, random_state=rns)
-            agg += [sample]
-            if head is not None and i >= head - 1:
-                break
-    except Exception:
-        return None
+    config = optimization_config or ClusteringOptimizationConfig()
 
-    if not agg:
+    if "embed" not in dfr.columns or "property" not in dfr.columns:
         return None
 
-    dfr = pd.concat(agg)
-
-    umap_columns = get_umap_columns(transform_config)
-
-    # Filter by selected labels if provided
     if selected_labels is not None:
         dfr = dfr.loc[dfr["property"].isin(selected_labels)].copy()
         if len(dfr) == 0:
             return None
 
-    # trim rare mentions
-    mention_count = dfr["property"].value_counts()
-    low_count_properties = mention_count[
-        ~(mention_count >= min_class_size)
-    ].index.to_list()
-
-    dfr = dfr.loc[~dfr["property"].isin(low_count_properties)].copy()
-
-    if len(dfr) == 0:
-        return None
+    if aggregation_level == "mention":
+        mention_count = dfr["property"].value_counts()
+        low_count_properties = mention_count[
+            ~(mention_count >= config.min_class_size)
+        ].index.to_list()
+        dfr = dfr.loc[~dfr["property"].isin(low_count_properties)].copy()
+        if len(dfr) == 0:
+            return None
 
-    # Get number of unique properties before transformation
-    number_properties = dfr["property"].nunique()
+    number_properties = int(dfr["property"].nunique())
 
+    umap_columns = get_umap_columns(transform_config)
     dfr2 = transform_embeddings(dfr, config=transform_config, embed_column="embed")
 
-    # Grid evaluation
-    sizes = list(np.arange(int(0.5 * min_class_size), max_scale, 5))
-
+    sizes = list(np.arange(int(0.5 * config.min_class_size), config.max_scale, 5))
     metrics_df = evaluate_cluster_size_grid(dfr2, umap_columns, sizes)
 
-    # Find optimal cluster size
     if all_metrics_dfs is not None:
-        # Aggregate with previous samples and find optimum from grid
         all_metrics_dfs.append(metrics_df)
         aggregated = aggregate_grid_metrics(all_metrics_dfs)
-        best_size, best_score, best_score_std = find_optimal_from_grid(
-            aggregated, method=optimization_method
+        solved = solve_optimal_min_cluster_size_from_aggregated(
+            aggregated,
+            method=config.optimization_method,
+            smooth_window=config.grid_smooth_window,
+            plateau_fraction=config.grid_plateau_fraction,
+            derivative_rel_tol=config.grid_derivative_rel_tol,
         )
+        best_size = solved.chosen_min_cluster_size
+        best_score = solved.score_mean_at_chosen
     else:
-        # Single sample: just use max DBCV from this sample
         if len(metrics_df) == 0:
             return None
         best_idx = metrics_df["dbcv"].idxmax()
         best_size = int(metrics_df.loc[best_idx, "min_cluster_size"])
         best_score = float(metrics_df.loc[best_idx, "dbcv"])
 
-    # Apply final clustering with best size
     clusterer = hdbscan.HDBSCAN(min_cluster_size=best_size, gen_min_span_tree=True)
     labels = clusterer.fit_predict(dfr2[umap_columns])
+    label_set = set(labels.tolist())
+    n_clusters_emergent = len(label_set) - (1 if -1 in label_set else 0)
     dfr2_final = dfr2.copy()
     dfr2_final["class"] = pd.DataFrame(
         labels, columns=["class"], index=dfr2_final.index
     )
 
-    # Compute Hungarian matching accuracy
-    # Use property column as true labels and class column as predicted clusters
-    hungarian_acc = None
+    ari_score = None
     if "property" in dfr2_final.columns and "class" in dfr2_final.columns:
-        # Convert property labels to numeric for confusion matrix
         property_labels = dfr2_final["property"].astype("category").cat.codes.values
         cluster_labels = dfr2_final["class"].values
-        hungarian_acc = compute_hungarian_accuracy(property_labels, cluster_labels)
+        ari_score = compute_adjusted_rand_index(property_labels, cluster_labels)
 
     return ClusteringReport(
-        best_size=best_size,
+        hyperparameters=ClusteringHyperparameters(min_cluster_size=best_size),
         best_score=best_score,
         number_properties=number_properties,
+        n_clusters_emergent=n_clusters_emergent,
         metrics_df=metrics_df,
         df=dfr2_final,
-        hungarian_accuracy=hungarian_acc,
+        ari=ari_score,
     )
 
 
+def estimate_model_clustering(
+    transform_config: TransformConfig,
+    optimization_config: ClusteringOptimizationConfig | None = None,
+    *,
+    file_path: pathlib.Path | None = None,
+    file_paths: Sequence[pathlib.Path] | None = None,
+    dfr: pd.DataFrame | None = None,
+    selected_labels: set[str] | None = None,
+    all_metrics_dfs: list[pd.DataFrame] | None = None,
+    embedding_read_status: Callable[[str], None] | None = None,
+    show_embedding_read_progress: bool = False,
+):
+    """
+    Estimate optimal cluster size from parquet file(s) or a preloaded DataFrame.
+
+    Provide exactly one of ``file_path``, ``file_paths``, or ``dfr``. For multiple parquets,
+    rows are inner-joined on (pmid, property, mention) and ``embed`` vectors are concatenated
+    in path order (must match ``EmbeddingModelMetadata.sources``). Sampling ``frac`` /
+    ``n_embedding_batches`` are applied while loading each file (batches), then ``frac`` is applied
+    once on the merged
+    mention-level frame.
+
+    Args:
+        transform_config: TransformConfig instance specifying transformation parameters
+        optimization_config: Clustering optimization settings. If None, defaults
+            to ClusteringOptimizationConfig().
+        file_path: Single parquet path (backward-compatible entry point).
+        file_paths: Multiple parquets to fuse at mention level before clustering.
+        dfr: Optional pre-built frame (e.g. fused) with ``property`` and ``embed`` columns.
+        selected_labels: Optional set of labels from selected labels KB to filter by
+        all_metrics_dfs: Optional list of metrics DataFrames from previous samples.
+        embedding_read_status: Callback for embedding parquet batch progress lines
+            (e.g. append to an existing Rich ``Progress`` task description).
+        show_embedding_read_progress: When True and ``embedding_read_status`` is omitted,
+            show a transient Rich progress display while loading parquet batches.
+
+    Returns:
+        ClusteringReport or None if processing failed
+    """
+    config = optimization_config or ClusteringOptimizationConfig()
+    rns: RandomState = config.rns
+
+    sources = [file_path is not None, file_paths is not None, dfr is not None]
+    if sum(bool(x) for x in sources) != 1:
+        raise ValueError(
+            "Provide exactly one of file_path=, file_paths=, or dfr= to estimate_model_clustering"
+        )
+
+    frame: pd.DataFrame | None
+    if dfr is not None:
+        frame = dfr.copy()
+    else:
+        if file_paths is not None:
+            paths = list(file_paths)
+        else:
+            assert file_path is not None
+            paths = [file_path]
+        if len(paths) == 0:
+            return None
+        frame = concat_mention_level_embedding_sources(
+            paths,
+            batch_size=config.batch_size,
+            n_embedding_batches=config.n_embedding_batches,
+            read_status=embedding_read_status,
+            show_read_progress=show_embedding_read_progress,
+        )
+        if frame is None or len(frame) == 0:
+            return None
+
+    frame = frame.sample(frac=config.frac, random_state=rns, replace=False)
+    if len(frame) == 0:
+        return None
+
+    return estimate_clustering_from_frame(
+        frame,
+        transform_config,
+        optimization_config=config,
+        selected_labels=selected_labels,
+        all_metrics_dfs=all_metrics_dfs,
+        aggregation_level="mention",
+    )
+
+
+def mention_frame_from_embedding_paths(
+    paths: Sequence[pathlib.Path],
+    *,
+    optimization_config: ClusteringOptimizationConfig | None = None,
+    read_status: Callable[[str], None] | None = None,
+    show_read_progress: bool = False,
+) -> pd.DataFrame | None:
+    """
+    Load mention-level rows from parquet file(s) like ``estimate_model_clustering``
+    (batched read, optional multi-source inner join on keys), without ``frac`` subsampling.
+    """
+    cfg = optimization_config or ClusteringOptimizationConfig()
+    return concat_mention_level_embedding_sources(
+        paths,
+        batch_size=cfg.batch_size,
+        n_embedding_batches=cfg.n_embedding_batches,
+        read_status=read_status,
+        show_read_progress=show_read_progress,
+    )
+
+
+def filter_mention_frame_by_kb_labels(
+    frame: pd.DataFrame,
+    kb_labels: set[str] | None,
+) -> pd.DataFrame:
+    """Restrict rows to ``property`` values present in ``kb_labels`` (skip if ``kb_labels`` is None)."""
+    if kb_labels is None:
+        return frame.copy()
+    return frame.loc[frame["property"].isin(kb_labels)].copy()
+
+
+def drop_properties_with_few_mentions(
+    frame: pd.DataFrame,
+    min_mentions_per_property: int,
+) -> pd.DataFrame:
+    """
+    Drop properties with fewer than ``min_mentions_per_property`` rows (same rule as
+    ``estimate_clustering_from_frame`` with ``aggregation_level='mention'``).
+    """
+    if "property" not in frame.columns:
+        raise ValueError("frame must contain a 'property' column")
+    mention_count = frame["property"].value_counts()
+    low_count = mention_count[
+        ~(mention_count >= min_mentions_per_property)
+    ].index.to_list()
+    return frame.loc[~frame["property"].isin(low_count)].copy()
+
+
 def embeddings_dict_to_dataframe(
     embeddings_dict: dict[str, tuple[str, torch.Tensor | np.ndarray]],
 ) -> pd.DataFrame:
diff --git a/pelinker/cli/fit.py b/pelinker/cli/fit.py
index dd30541..db1cb0f 100644
--- a/pelinker/cli/fit.py
+++ b/pelinker/cli/fit.py
@@ -1,263 +1,474 @@
-# pylint: disable=E1120
-
-import click
-import pathlib
 import logging
-import tempfile
-import pandas as pd
-
-from pelinker.model import Linker
-from pelinker.embedder import embed_kb_corpus
-from pelinker.transform import TransformConfig
+from dataclasses import dataclass
+from datetime import date
 from importlib.resources import files
 from pathlib import Path
+from typing import Any, Literal, cast
+
+import hydra
+import pandas as pd
+from hydra.core.config_store import ConfigStore
 from numpy.random import RandomState
-from pelinker.util import str2layers, layers2str
+from omegaconf import MISSING, OmegaConf
+
+from pelinker.clustering_quality_checkpoint import combination_key_from_members
+from pelinker.config import (
+    ClusteringOptimizationConfig,
+    EmbeddingModelMetadata,
+    EmbeddingSourceSpec,
+    EmbeddingTrainingConfig,
+    KBConfig,
+    TransformConfig,
+)
+from pelinker.embedder import embed_kb_corpus
+from pelinker.model import Linker
+from pelinker.util import expand_config_path, layers2str, str2layers
 
 logger = logging.getLogger(__name__)
 
+FitPipeline = Literal["auto", "embed_only", "fit_only", "both"]
+_PIPELINE_VALUES: frozenset[str] = frozenset(("auto", "embed_only", "fit_only", "both"))
 
-@click.command()
-@click.option(
-    "--model-type",
-    type=click.STRING,
-    default="pubmedbert",
-    help="run over BERT flavours",
-)
-@click.option(
-    "--layers-spec",
-    type=click.STRING,
-    default="1",
-    help="`sent` or a string of layers, `1,2,3` would correspond to layers [-1, -2, -3]",
-)
-@click.option(
-    "--kb-path",
-    type=click.Path(path_type=pathlib.Path),
-    help="Path to KB CSV file.",
-)
-@click.option(
-    "--pca-components",
-    type=click.INT,
-    default=100,
-    help="Number of PCA components for dimensionality reduction",
+# Longest first so e.g. ``biobert`` does not steal a match from ``biobert-stsb``.
+_KNOWN_EMBEDDING_MODEL_TYPES: tuple[str, ...] = (
+    "biobert-stsb",
+    "pubmedbert",
+    "bluebert",
+    "scibert",
+    "biobert",
+    "bert",
 )
-@click.option(
-    "--umap-dim",
-    type=click.INT,
-    default=8,
-    help="UMAP dimensionality for clustering (range: 3-5 recommended)",
-)
-@click.option(
-    "--min-class-size",
-    type=click.INT,
-    default=20,
-    help="Minimum class size for filtering",
-)
-@click.option(
-    "--max-scale",
-    type=click.INT,
-    default=120,
-    help="Maximum value for grid evaluation of min_cluster_size",
-)
-@click.option(
-    "--output-path",
-    type=click.Path(path_type=pathlib.Path),
-    help="Output path for saved model. If not provided, uses default in pelinker.store",
-)
-@click.option(
-    "--input-text-table-path",
-    type=click.Path(path_type=pathlib.Path),
-    help="Input file (TSV/CSV, optionally gzipped) with pmid and text columns. If not provided, will be required.",
-)
-@click.option(
-    "--use-gpu",
-    is_flag=True,
-    default=False,
-    help="Enable GPU acceleration if CUDA is available",
-)
-@click.option(
-    "--chunk-size",
-    type=click.INT,
-    default=1000,
-    help="Chunk size for streaming input. Each chunk is split into batches and serialized.",
-)
-@click.option(
-    "--batch-size",
-    type=click.INT,
-    default=200,
-    help="Embedding batch size inside a chunk.",
-)
-@click.option(
-    "--nlp-model",
-    type=click.STRING,
-    default="en_core_web_trf",
-    help="spaCy model to use for tokenization/lemmas.",
-)
-@click.option(
-    "--head",
-    type=click.INT,
-    default=None,
-    help="Number of chunks to process (skip it for all chunks).",
-)
-@click.option(
-    "--embeddings-path",
-    type=click.Path(path_type=pathlib.Path),
-    default=None,
-    help="Optional path to pre-computed embeddings parquet file. If provided, skips embedding step (a) and goes directly to clustering (b).",
-)
-def fit(
+
+
+@dataclass
+class FitCliConfig:
+    """Hydra config for ``python -m pelinker.cli.fit``."""
+
+    model_type: str = "pubmedbert"
+    layers_spec: str = "1"
+    kb_path: str = MISSING
+    pca_components: int = 100
+    umap_dim: int = 8
+    min_class_size: int = 20
+    max_scale: int = 120
+    output_path: str | None = None
+    # Mention-level parquet path(s). Order matches ``embedding_metadata.sources`` (see fit() docstring).
+    embeddings_parquet: Any = MISSING
+    input_text_table_path: str | None = None
+    use_gpu: bool = False
+    nlp_model: str = "en_core_web_trf"
+    # Stage (A): text table I/O buffer rows, encoder batch size (GPU), optional cap on read passes.
+    input_buffer_rows: int = 1000
+    encoder_batch_size: int = 200
+    max_input_buffers: int | None = None
+    # Stage (B): parquet batching (``batch_size`` rows per read batch).
+    # ``frac`` subsamples rows only for ``min_cluster_size`` grid search when ``optimize_clustering`` is on; final fit uses all prepared rows.
+    frac: float = 1.0
+    n_embedding_batches: int | None = None  # max read batches per parquet; None = all
+    batch_size: int = 1000
+    # RNG seed for grid-search row subsampling (``ClusteringOptimizationConfig.rns``); final fit is not subsampled.
+    clustering_seed: int = 13
+    kb_name: str | None = None
+    kb_version: str = "0.1.0"
+    kb_created_at: str | None = None
+    kb_description: str = ""
+    kb_entity_count: int | None = None
+    # Discriminator: auto = fit from parquet only if no text table; else embed then fit (legacy).
+    # str (not Literal): OmegaConf structured configs reject Literal annotations on fields.
+    pipeline: str = "embed_only"
+    # Per-parquet backbone/layer (length 1 broadcast, or same length as ``embeddings_parquet``).
+    # When omitted, ``model_type`` / ``layers_spec`` scalars apply unless the parquet stem matches
+    # ``..._<model>_<layers>`` (see ``_parse_embedding_parquet_stem``).
+    model_types: list[str] | None = None
+    layers_specs: list[str] | None = None
+
+    def __post_init__(self) -> None:
+        if self.pipeline not in _PIPELINE_VALUES:
+            raise ValueError(
+                "pipeline must be one of "
+                f"{sorted(_PIPELINE_VALUES)}, got {self.pipeline!r}"
+            )
+
+
+def _coerce_str_list(val: object) -> list[str]:
+    if val is None or val is MISSING:
+        return []
+    if isinstance(val, str):
+        return [val]
+    resolved = OmegaConf.to_container(val, resolve=True)
+    if isinstance(resolved, list):
+        return [str(x) for x in resolved]
+    return [str(resolved)]
+
+
+def _coerce_optional_str_list(val: object) -> list[str] | None:
+    if val is None or val is MISSING:
+        return None
+    if isinstance(val, str):
+        return [val]
+    resolved = OmegaConf.to_container(val, resolve=True)
+    if isinstance(resolved, list):
+        out = [str(x) for x in resolved]
+        return out if out else None
+    return [str(resolved)]
+
+
+def _parquet_stem_for_embedding_meta(path: Path) -> str:
+    """Filename stem used to infer backbone/layers (supports ``.parquet`` and ``.parquet.gz``)."""
+    name = path.name
+    if name.endswith(".parquet.gz"):
+        return name[: -len(".parquet.gz")]
+    if name.endswith(".parquet"):
+        return name[: -len(".parquet")]
+    return path.stem
+
+
+def _normalize_layers_filename_part(layer_part: str) -> str | None:
+    """
+    Map a filename layer segment to ``layers_spec``.
+
+    Accepts a compact digit string (e.g. ``12`` → layers 1 and 2) or underscore-separated
+    indices as produced by ``run/loop.fit.sh`` (``1_2_3`` → ``1,2,3``).
+    """
+    s = layer_part.strip()
+    if not s:
+        return None
+    if "_" in s:
+        parts = s.split("_")
+        if not parts or not all(p.isdigit() for p in parts):
+            return None
+        return ",".join(parts)
+    if s.isdigit():
+        return s
+    return None
+
+
+def _parse_embedding_parquet_stem(stem: str) -> tuple[str, str] | None:
+    """
+    Parse ``<prefix>_<model>_<layers>`` or ``<model>_<layers>`` (``layers`` as in
+    ``res_pubmedbert_1.parquet`` / ``res_pubmedbert_1_2_3.parquet``).
+    """
+    for mt in _KNOWN_EMBEDDING_MODEL_TYPES:
+        needle = f"_{mt}_"
+        idx = stem.rfind(needle)
+        if idx >= 0:
+            layer_part = stem[idx + len(needle) :]
+            ls = _normalize_layers_filename_part(layer_part)
+            if ls is not None:
+                return mt, ls
+        prefix = f"{mt}_"
+        if stem.startswith(prefix):
+            layer_part = stem[len(prefix) :]
+            ls = _normalize_layers_filename_part(layer_part)
+            if ls is not None:
+                return mt, ls
+    return None
+
+
+def _parse_embedding_parquet_path(path: Path) -> tuple[str, str] | None:
+    return _parse_embedding_parquet_stem(_parquet_stem_for_embedding_meta(path))
+
+
+def _broadcast_model_types(
+    model_type: str, model_types: list[str] | None, n: int
+) -> tuple[str, ...]:
+    if model_types is None:
+        return (model_type,) * n
+    if len(model_types) == 1 and n > 1:
+        return (model_types[0],) * n
+    if len(model_types) == n:
+        return tuple(model_types)
+    raise ValueError(
+        f"model_types must have length 1 or {n} (one per parquet), got {len(model_types)}"
+    )
+
+
+def _broadcast_layers_specs(
+    layers_spec: str, layers_specs: list[str] | None, n: int
+) -> tuple[str, ...]:
+    if layers_specs is None:
+        return (layers_spec,) * n
+    if len(layers_specs) == 1 and n > 1:
+        return (layers_specs[0],) * n
+    if len(layers_specs) == n:
+        return tuple(layers_specs)
+    raise ValueError(
+        f"layers_specs must have length 1 or {n} (one per parquet), got {len(layers_specs)}"
+    )
+
+
+def _embedding_metadata(
+    embed_paths: list[Path],
     model_type: str,
     layers_spec: str,
-    kb_path: pathlib.Path | None,
-    pca_components: int,
-    umap_dim: int,
-    min_class_size: int,
-    max_scale: int,
-    output_path: pathlib.Path | None,
-    input_text_table_path: pathlib.Path | None,
-    use_gpu: bool,
-    chunk_size: int,
-    batch_size: int,
-    nlp_model: str,
-    head: int | None,
-    embeddings_path: pathlib.Path | None,
-):
+    model_types: list[str] | None,
+    layers_specs: list[str] | None,
+) -> EmbeddingModelMetadata:
+    n = len(embed_paths)
+    if n < 1:
+        raise ValueError("At least one embeddings parquet path is required")
+
+    parsed_per_path = [_parse_embedding_parquet_path(p) for p in embed_paths]
+
+    if model_types is not None:
+        mts = _broadcast_model_types(model_type, model_types, n)
+    else:
+        mts = tuple(pc[0] if pc is not None else model_type for pc in parsed_per_path)
+
+    if layers_specs is not None:
+        lss = _broadcast_layers_specs(layers_spec, layers_specs, n)
+    else:
+        lss = tuple(pc[1] if pc is not None else layers_spec for pc in parsed_per_path)
+
+    for p, pc in zip(embed_paths, parsed_per_path):
+        if pc is None:
+            continue
+        inferred_bits: list[str] = []
+        if model_types is None:
+            inferred_bits.append(f"model_type={pc[0]!r}")
+        if layers_specs is None:
+            inferred_bits.append(f"layers_spec={pc[1]!r}")
+        if inferred_bits:
+            logger.info(
+                "Inferred %s from parquet filename %s",
+                ", ".join(inferred_bits),
+                p.name,
+            )
+
+    return EmbeddingModelMetadata(
+        sources=tuple(EmbeddingSourceSpec(m, ls) for m, ls in zip(mts, lss))
+    )
+
+
+def _default_model_store_name(meta: EmbeddingModelMetadata) -> str:
+    if len(meta.sources) == 1:
+        s0 = meta.sources[0]
+        return (
+            f"pelinker.model.{s0.model_type}.{layers2str(str2layers(s0.layers_spec))}"
+        )
+    members = [(s.model_type, s.layers_spec) for s in meta.sources]
+    key = combination_key_from_members(members)
+    safe = "".join(c if c.isalnum() or c in "-_" else "_" for c in key)
+    return f"pelinker.model.{safe}"
+
+
+def _abort_if_outputs_exist(paths: list[Path], *, context: str) -> None:
+    existing = [p for p in paths if p.is_file()]
+    if not existing:
+        return
+    logger.warning(
+        "%s: refusing to write — %s already exist(s): %s",
+        context,
+        "file" if len(existing) == 1 else "files",
+        existing,
+    )
+    raise SystemExit(1)
+
+
+def fit(cfg: FitCliConfig) -> None:
     """
-    Fit a Linker model by embedding corpus and selecting KB entities.
+    Run embedding (optional), fit a ``Linker`` from parquet(s) (optional), and save the model (optional).
 
-    This follows the logic of:
-    1. embed_kb_corpus.py - for embedding the whole corpus (step a)
-    2. Filtering to only KB entities and aggregating embeddings per property
-    3. clustering_quality.py - for creating clustering model (step b)
+    - ``pipeline=auto``: if ``input_text_table_path`` is set, embed then fit (unless outputs exist);
+      if unset, fit only from existing parquet(s).
+    - ``pipeline=embed_only``: write parquet(s) only.
+    - ``pipeline=fit_only``: load mention-level parquet(s), train like ``estimate_model_clustering`` / ``Linker.fit``, save.
+    - ``pipeline=both``: require a text table; write parquet(s) then fit and save.
 
-    If --embeddings-path is provided, step (a) is skipped and we go directly to step (b).
+    Multiple values for ``embeddings_parquet`` fuse mention-level files in list order (inner join on
+    pmid/property/mention, same as ``estimate_model_clustering``). Set ``model_types`` /
+    ``layers_specs`` (or scalars ``model_type`` / ``layers_spec``) so ``embedding_metadata.sources``
+    matches that order. If a list field is omitted, each path may supply ``model_type`` and/or
+    ``layers_spec`` via a matching filename stem (``..._<model>_<layers>.parquet``, as in
+    ``run/loop.fit.sh``); otherwise the scalar defaults apply for that component.
     """
     logging.basicConfig(
         level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
     )
 
-    kb_path = kb_path.expanduser()
-    logger.info(f"Using KB: {kb_path}")
+    kb_path = expand_config_path(cfg.kb_path)
+    if kb_path is None:
+        raise ValueError("kb_path must be provided")
+    logger.info("Using KB: %s", kb_path)
 
     df0 = pd.read_csv(kb_path)
 
-    logger.info(f"Loaded {len(df0)} properties from KB")
+    logger.info("Loaded %s properties from KB", len(df0))
+
+    if "entity_id" not in df0.columns:
+        raise ValueError(
+            "KB CSV must contain an 'entity_id' column "
+            "(see run/embed_kb_corpus --kb-csv-path)."
+        )
+    if "label" not in df0.columns:
+        raise ValueError("KB CSV must contain a 'label' column.")
+
+    labels_map: dict[str, str] = {
+        str(eid): str(lbl)
+        for eid, lbl in zip(df0["entity_id"], df0["label"])
+        if pd.notna(lbl)
+    }
 
-    # Extract property labels from KB for corpus embedding
     kb_labels = set(df0["label"].dropna().unique())
 
-    logger.info(f"Extracted {len(kb_labels)} unique property labels from KB")
+    logger.info("Extracted %s unique property labels from KB", len(kb_labels))
 
-    # Create transform config
     transform_config = TransformConfig(
-        pca_components=pca_components,
-        umap_components=umap_dim,
+        pca_components=cfg.pca_components,
+        umap_components=cfg.umap_dim,
     )
 
-    # Part a) Embed corpus (skip if embeddings_path is provided)
-    if embeddings_path is None:
-        # Embeddings path not provided, need to run embedding step
-        if input_text_table_path is None:
-            raise ValueError(
-                "Either --input-text-table-path or --embeddings-path must be provided"
-            )
+    input_text_table_path = expand_config_path(cfg.input_text_table_path)
+    output_path = expand_config_path(cfg.output_path)
 
-        input_text_table_path = input_text_table_path.expanduser()
+    path_strs = _coerce_str_list(cfg.embeddings_parquet)
+    if not path_strs:
+        raise ValueError("embeddings_parquet must be one or more paths")
 
-        # Create temporary output parquet path
-        with tempfile.NamedTemporaryFile(suffix=".parquet", delete=False) as f:
-            output_parquet_path = Path(f.name)
+    embed_paths: list[Path] = []
+    for s in path_strs:
+        p = expand_config_path(s)
+        if p is None:
+            raise ValueError(f"Invalid embeddings path: {s!r}")
+        embed_paths.append(p)
 
-        try:
-            logger.info("Step (a): Embedding corpus...")
-            embed_kb_corpus(
-                model_type=model_type,
-                layers_spec=layers_spec,
-                input_text_table_path=input_text_table_path,
-                kb_csv_path=kb_path,
-                output_parquet_path=output_parquet_path,
-                use_gpu=use_gpu,
-                chunk_size=chunk_size,
-                batch_size=batch_size,
-                nlp_model=nlp_model,
-                head=head,
-            )
-            embeddings_path = output_parquet_path
-            is_temporary = True
-        except Exception as e:
-            # Clean up temporary file on error
-            try:
-                output_parquet_path.unlink()
-            except Exception:
-                pass
-            raise e
+    mts = _coerce_optional_str_list(cfg.model_types)
+    lss = _coerce_optional_str_list(cfg.layers_specs)
+    embedding_metadata = _embedding_metadata(
+        embed_paths, cfg.model_type, cfg.layers_spec, mts, lss
+    )
+
+    pipeline = cfg.pipeline
+    if pipeline == "auto":
+        effective: FitPipeline = "fit_only" if input_text_table_path is None else "both"
     else:
-        # Use provided embeddings path
-        embeddings_path = embeddings_path.expanduser()
-        logger.info(
-            f"Step (a): Skipping embedding, using provided embeddings: {embeddings_path}"
-        )
-        is_temporary = False
+        effective = cast(FitPipeline, pipeline)
 
-    # Part b) Fit clustering model
-    try:
-        logger.info("Step (b): Fitting clustering model...")
+    if effective == "fit_only" and input_text_table_path is not None:
+        raise ValueError(
+            "pipeline=fit_only (or auto with no text table): omit input_text_table_path."
+        )
+    if effective in ("both", "embed_only") and input_text_table_path is None:
+        raise ValueError(
+            f"pipeline={effective} requires input_text_table_path for stage (A)."
+        )
 
-        # Create Linker model
-        layers = str2layers(layers_spec)
-        layers_str = layers2str(layers)
+    if effective == "fit_only":
+        missing = [p for p in embed_paths if not p.is_file()]
+        if missing:
+            raise FileNotFoundError(
+                f"Embedding parquet(s) not found for fit_only: {missing}"
+            )
 
-        # Initialize with empty vocabulary (will be set during fit)
-        linker = Linker(
-            layers=layers,
-            transform_config=transform_config,
+    if effective == "both":
+        _abort_if_outputs_exist(
+            embed_paths,
+            context="pipeline=both: embeddings_parquet target(s)",
         )
-
-        # Fit the linker - this handles loading embeddings, filtering, aggregation, and clustering
-        linker.fit(
-            embeddings=embeddings_path,
-            transform_config=transform_config,
-            min_cluster_size=min_class_size,
-            kb_labels=kb_labels,
-            optimize_clustering=True,
-            clustering_optimization_params={
-                "min_class_size": min_class_size,
-                "max_scale": max_scale,
-                "rns": RandomState(seed=13),
-                "frac": 1.0,
-                "head": None,
-                "batch_size": chunk_size,
-                "optimization_method": "mean",
-            },
+    elif effective == "embed_only":
+        _abort_if_outputs_exist(
+            embed_paths,
+            context="pipeline=embed_only",
         )
 
-        logger.info(f"Fitted Linker model with {len(linker.vocabulary)} entities")
+    if effective in ("both", "embed_only"):
         logger.info(
-            f"Number of clusters: {len(set(linker.cluster_assignments.values()))}"
+            "Stage (A): embed_kb_corpus → %s",
+            embed_paths if len(embed_paths) > 1 else embed_paths[0],
         )
-
-        # Save model
-        if output_path is None:
-            file_spec = files("pelinker.store").joinpath(
-                f"pelinker.model.{model_type}.{layers_str}"
+        training = EmbeddingTrainingConfig(
+            input_text_table_path=input_text_table_path,
+            kb_csv_path=kb_path,
+            use_gpu=cfg.use_gpu,
+            input_buffer_rows=cfg.input_buffer_rows,
+            encoder_batch_size=cfg.encoder_batch_size,
+            nlp_model=cfg.nlp_model,
+            max_input_buffers=cfg.max_input_buffers,
+        )
+        if len(embed_paths) == 1:
+            embed_kb_corpus(
+                metadata=embedding_metadata,
+                training=training,
+                output_parquet_path=embed_paths[0],
             )
         else:
-            file_spec = output_path.expanduser()
+            embed_kb_corpus(
+                metadata=embedding_metadata,
+                training=training,
+                output_parquet_paths=tuple(embed_paths),
+            )
+
+    if effective == "embed_only":
+        logger.info("Embed-only pipeline finished; not fitting or saving a linker.")
+        return
+
+    clustering_config = ClusteringOptimizationConfig(
+        min_class_size=cfg.min_class_size,
+        max_scale=cfg.max_scale,
+        rns=RandomState(cfg.clustering_seed),
+        frac=cfg.frac,
+        n_embedding_batches=cfg.n_embedding_batches,
+        batch_size=cfg.batch_size,
+    )
+
+    kb_created = (
+        date.fromisoformat(cfg.kb_created_at) if cfg.kb_created_at else date.today()
+    )
+    kb_display_name = (cfg.kb_name or "").strip() or kb_path.stem
+    kb_config = KBConfig(
+        name=kb_display_name,
+        version=cfg.kb_version,
+        created_at=kb_created,
+        description=cfg.kb_description,
+        entity_count=cfg.kb_entity_count,
+    )
+
+    linker = Linker(
+        labels_map=labels_map,
+        transform_config=transform_config,
+        embedding_metadata=embedding_metadata,
+    )
+
+    logger.info("Stage (B): Linker.fit from %s", embed_paths)
+    linker.fit(
+        embeddings=embed_paths if len(embed_paths) > 1 else embed_paths[0],
+        transform_config=transform_config,
+        min_cluster_size=cfg.min_class_size,
+        kb_labels=kb_labels,
+        optimize_clustering=True,
+        clustering_optimization_config=clustering_config,
+        embedding_training=None,
+        kb_config=kb_config,
+    )
+
+    logger.info("Fitted Linker model with %s entities", len(linker.vocabulary))
+    logger.info(
+        "Number of clusters: %s",
+        len(set(linker.cluster_assignments.values())),
+    )
+
+    if output_path is None:
+        file_spec = files("pelinker.store").joinpath(
+            _default_model_store_name(embedding_metadata)
+        )
+    else:
+        file_spec = output_path
+
+    logger.info("Saving model to %s", file_spec)
+    linker.dump(file_spec)
+
+    logger.info("Model saved successfully!")
+
 
-        logger.info(f"Saving model to {file_spec}")
-        linker.dump(file_spec)
+CONFIG_STORE = ConfigStore.instance()
+CONFIG_STORE.store(name="fit_config", node=FitCliConfig)
 
-        logger.info("Model saved successfully!")
 
-    finally:
-        # Clean up temporary file if we created it
-        if is_temporary:
-            try:
-                embeddings_path.unlink()
-                logger.debug(f"Removed temporary parquet file: {embeddings_path}")
-            except Exception as e:
-                logger.warning(f"Failed to remove temporary parquet file: {e}")
+@hydra.main(version_base=None, config_path="pkg://pelinker.conf", config_name="fit")
+def run(cfg: FitCliConfig) -> None:
+    logger.info("Running fit with config:\n%s", OmegaConf.to_yaml(cfg))
+    fit(cfg)
 
 
 if __name__ == "__main__":
-    fit()
+    run()
diff --git a/pelinker/cli/server.py b/pelinker/cli/server.py
index 6a1c565..f0bff18 100644
--- a/pelinker/cli/server.py
+++ b/pelinker/cli/server.py
@@ -1,156 +1,221 @@
-import logging.config
-
-import click
-from flask import Flask, jsonify, request
-from flask_compress import Compress
-from flask_cors import cross_origin
-from flask_restful import Api
-from waitress import serve
+from __future__ import annotations
+
+import logging
+from contextlib import asynccontextmanager
+from dataclasses import asdict, dataclass, field
+from datetime import date
 from importlib.resources import files
-from pelinker.util import load_models, str2layers, layers2str
-from pelinker.onto import MAX_LENGTH
+from pathlib import Path
+from typing import Annotated, Any
+
+import hydra
+import uvicorn
+from fastapi import Depends, FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from hydra.core.config_store import ConfigStore
+from omegaconf import OmegaConf
+from pydantic import BaseModel, ConfigDict, Field
+from starlette.middleware.gzip import GZipMiddleware
+
+from pelinker.config import EmbeddingModelMetadata, KBConfig
 from pelinker.model import Linker
-from pprint import pprint
-import pathlib
-import spacy
-import json
-
-app = Flask(__name__)
-Compress(app)
-api = Api(app)
+from pelinker.onto import MAX_LENGTH
+from pelinker.util import str2layers, layers2str
 
 logger = logging.getLogger(__name__)
 
 
-@click.command()
-@click.option("--port", type=click.INT, default=8599)
-@click.option("--host", type=click.STRING, default="0.0.0.0")
-@click.option(
-    "--model-type",
-    type=click.STRING,
-    default="pubmedbert",
-    help="run over BERT flavours",
-)
-@click.option(
-    "--superposition",
-    type=click.BOOL,
-    default=False,
-    help="use a superposition of label and description embeddings, where available",
-)
-@click.option("--extra-context", type=click.BOOL, is_flag=True, default=False)
-@click.option(
-    "--layers-spec",
-    default="sent",
-    type=click.STRING,
-    help="`sent` or a string of layers, `1,2,3` would correspond to layers [-1, -2, -3]",
-)
-@click.option("--thr-score", type=click.FLOAT, default=0.5)
-@click.option("--thr-dif", type=click.FLOAT, default=0.0)
-@click.option(
-    "--sample-path",
-    type=click.Path(path_type=pathlib.Path),
-    default=None,
-    help="json with test docs",
-)
-def main(
-    model_type,
-    layers_spec,
-    superposition,
-    port,
-    host,
-    extra_context,
-    thr_score,
-    thr_dif,
-    sample_path,
-):
-    logger_conf = "logging.conf"
-    logging.config.fileConfig(logger_conf, disable_existing_loggers=False)
-    app.logger.setLevel(logging.INFO)
-
-    logger.info(f"thr_score : {thr_score}, thr_dif: {thr_dif}")
-
-    layers = str2layers(layers_spec)
-    sentence = True if layers == "sent" else False
-    suffix = ".superposition" if superposition else ""
+@dataclass
+class ServerCliConfig:
+    """Hydra/OmegaConf node for the linker HTTP server (aligned with ``fit`` CLI)."""
+
+    host: str = "0.0.0.0"
+    port: int = 8599
+    """Path to the dumped linker **without** ``.gz`` (same as ``Linker.dump`` / ``Linker.load``). If omitted, uses the packaged store path built from ``model_type`` and ``layers_spec``."""
+    model_file_spec: str | None = None
+    model_type: str = "pubmedbert"
+    layers_spec: str = "1"
+    thr_score: float = 0.5
+    use_gpu: bool = False
+    cors_allow_origins: list[str] = field(default_factory=lambda: ["*"])
+
+
+class LinkRequest(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    text: str = Field(..., min_length=1)
+    thr_score: float | None = None
+    use_gpu: bool | None = None
+
+
+class ServerState:
+    def __init__(
+        self,
+        *,
+        linker: Linker,
+        nlp: Any,
+        cfg: ServerCliConfig,
+        resolved_model_path: str,
+    ) -> None:
+        self.linker = linker
+        self.nlp = nlp
+        self.cfg = cfg
+        self.resolved_model_path = resolved_model_path
+
+
+def _resolve_model_file_spec(cfg: ServerCliConfig) -> Any:
+    if cfg.model_file_spec:
+        p = Path(cfg.model_file_spec).expanduser()
+        if p.suffix == ".gz":
+            p = p.with_suffix("")
+        return p
+    layers = str2layers(cfg.layers_spec)
     layers_str = layers2str(layers)
-
-    model_spec = files("pelinker.store").joinpath(
-        f"pelinker.model.{model_type}.{layers_str}{suffix}"
+    return files("pelinker.store").joinpath(
+        f"pelinker.model.{cfg.model_type}.{layers_str}"
     )
 
-    logger.info(f"model path: {model_spec}")
-    pe_model: Linker = Linker.load(model_spec)
-
-    logger.info(f"pelinker model loaded : {pe_model}")
-
-    tokenizer, model = load_models(model_type, sentence=sentence)
-    logger.info(f"tokenizer and model loaded : {model}")
-
-    nlp = spacy.load("en_core_web_trf")
-    logger.info(f"spacy model loaded : {nlp}")
-
-    @app.route("/link", methods=["POST"])
-    @cross_origin()
-    def link():
-        """
-            navigate api
-                1. converts incoming text to a graph
-                2. computes metrics of the resultant graph wrt to the literature KG
-                3. fetches the graphs of most relevant publications from the literature KG
-
-        :return: response
-        """
-        if request.method == "POST":
-            json_data = request.json
-            try:
-                text = json_data["text"]
-                thr_score0 = json_data.pop("thr_score", thr_score)
-                thr_dif0 = json_data.pop("thr_dif", thr_dif)
-                r = pe_model.predict(
-                    text,
-                    tokenizer,
-                    model,
-                    nlp,
-                    MAX_LENGTH,
-                    topk=None,
-                    extra_context=extra_context,
-                )
-                r = Linker.filter_report(r, thr_score=thr_score0, thr_dif=thr_dif0)
-
-            except Exception as exc:
-                logger.error(f"{exc}")
-                return {"error": str(exc)}, 202
-
-            try:
-                json_response = jsonify(r)
-            except Exception as exc:
-                logger.error(f"{exc}")
-                return {"error": str(exc)}, 201
-
-            return json_response, 200
-
-    if sample_path is not None:
-        with open(sample_path) as json_file:
-            sample_data = json.load(json_file)
-        text = sample_data["text"][:11]
-        r = pe_model.predict(
-            text,
-            tokenizer,
-            model,
-            nlp,
-            MAX_LENGTH,
-            topk=None,
-            extra_context=extra_context,
+
+def _load_linker(cfg: ServerCliConfig) -> tuple[Linker, str]:
+    file_spec = _resolve_model_file_spec(cfg)
+    resolved = str(file_spec)
+    try:
+        linker = Linker.load(file_spec)
+    except FileNotFoundError as exc:
+        raise FileNotFoundError(
+            f"No dumped model at {resolved!s}.gz (check model_file_spec or model_type/layers_spec)."
+        ) from exc
+
+    if linker.embedding_metadata is None:
+        linker.embedding_metadata = EmbeddingModelMetadata.from_single(
+            cfg.model_type, cfg.layers_spec
+        )
+        logger.warning(
+            "Loaded linker had no embedding_metadata; using cfg: model_type=%r layers_spec=%r",
+            cfg.model_type,
+            cfg.layers_spec,
+        )
+    return linker, resolved
+
+
+def _kb_to_jsonable(kb: KBConfig | None) -> dict[str, Any] | None:
+    if kb is None:
+        return None
+    d = asdict(kb)
+    created = d["created_at"]
+    if isinstance(created, date):
+        d["created_at"] = created.isoformat()
+    return d
+
+
+def _transform_to_jsonable(linker: Linker) -> dict[str, Any] | None:
+    tc = linker.transform_config
+    if tc is None:
+        return None
+    return asdict(tc)
+
+
+def build_info_payload(state: ServerState) -> dict[str, Any]:
+    linker = state.linker
+    em = linker.embedding_metadata
+    sources_json: list[dict[str, str]] = []
+    if em is not None:
+        sources_json = [
+            {"model_type": s.model_type, "layers_spec": s.layers_spec}
+            for s in em.sources
+        ]
+    cluster_ids = set(linker.cluster_assignments.values())
+    return {
+        "resolved_model_path": state.resolved_model_path,
+        "embedding_metadata": {"sources": sources_json} if em is not None else None,
+        "kb": _kb_to_jsonable(linker.kb_config),
+        "vocabulary_size": len(linker.vocabulary),
+        "cluster_count": len(cluster_ids),
+        "transform_config": _transform_to_jsonable(linker),
+    }
+
+
+def create_app(cfg: ServerCliConfig) -> FastAPI:
+    state_holder: dict[str, ServerState | None] = {"state": None}
+
+    @asynccontextmanager
+    async def lifespan(app: FastAPI):
+        logging.basicConfig(
+            level=logging.INFO,
+            format="%(asctime)s - %(levelname)s - %(message)s",
         )
-        r = Linker.filter_report(r, thr_score=thr_score, thr_dif=thr_dif)
-        pprint(r)
-    else:
-        serve(
-            app,
-            host=host,
-            port=port,
+        linker, resolved = _load_linker(cfg)
+        logger.info("Loaded linker from %s.gz", resolved)
+        state_holder["state"] = ServerState(
+            linker=linker,
+            cfg=cfg,
+            resolved_model_path=resolved,
         )
+        yield
+        state_holder["state"] = None
+
+    app = FastAPI(title="pelinker", lifespan=lifespan)
+    app.add_middleware(GZipMiddleware, minimum_size=512)
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=list(cfg.cors_allow_origins),
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+    def get_state() -> ServerState:
+        s = state_holder["state"]
+        if s is None:
+            raise HTTPException(status_code=503, detail="Server not ready")
+        return s
+
+    StateDep = Annotated[ServerState, Depends(get_state)]
+
+    @app.get("/info")
+    def info(state: StateDep) -> dict[str, Any]:
+        return build_info_payload(state)
+
+    @app.post("/link")
+    def link(body: LinkRequest, state: StateDep) -> dict[str, Any]:
+        thr_s = body.thr_score if body.thr_score is not None else state.cfg.thr_score
+        use_gpu = body.use_gpu if body.use_gpu is not None else state.cfg.use_gpu
+        try:
+            r = state.linker.predict(
+                [body.text],
+                state.nlp,
+                MAX_LENGTH,
+                threshold=0.0,
+                use_gpu=use_gpu,
+            )
+            r = Linker.filter_report(r, thr_score=thr_s)
+        except Exception as exc:
+            logger.exception("link failed")
+            raise HTTPException(status_code=500, detail=str(exc)) from exc
+        return r
+
+    return app
+
+
+def serve(cfg: ServerCliConfig) -> None:
+    app = create_app(cfg)
+    logger.info("Starting uvicorn on %s:%s", cfg.host, cfg.port)
+    uvicorn.run(app, host=cfg.host, port=cfg.port, log_level="info")
+
+
+CONFIG_STORE = ConfigStore.instance()
+CONFIG_STORE.store(name="server_config", node=ServerCliConfig)
+
+
+@hydra.main(version_base=None, config_path="pkg://pelinker.conf", config_name="server")
+def run(cfg: ServerCliConfig) -> None:
+    logger.info("Running server with config:\n%s", OmegaConf.to_yaml(cfg))
+    try:
+        serve(cfg)
+    except FileNotFoundError as exc:
+        logger.error("%s", exc)
+        raise SystemExit(1) from exc
 
 
 if __name__ == "__main__":
-    main()
+    run()
diff --git a/pelinker/clustering_fusion_ranking.py b/pelinker/clustering_fusion_ranking.py
new file mode 100644
index 0000000..2e6eed5
--- /dev/null
+++ b/pelinker/clustering_fusion_ranking.py
@@ -0,0 +1,66 @@
+"""Rank single-embedding runs for fusion experiments (DBCV proxy before fused clustering)."""
+
+from __future__ import annotations
+
+import pathlib
+from itertools import combinations
+
+
+def singleton_items_by_dbcv_score(
+    valid_files: list[tuple[pathlib.Path, str, str]],
+    score_by_model_layer: dict[tuple[str, str], float],
+) -> list[tuple[pathlib.Path, str, str, float]]:
+    """
+    One tuple per (model, layer) that has a score, with path and mean DBCV, best-first.
+    """
+    rows: list[tuple[pathlib.Path, str, str, float]] = []
+    for fp, model, layer in valid_files:
+        key = (model, layer)
+        if key in score_by_model_layer:
+            rows.append((fp, model, layer, float(score_by_model_layer[key])))
+    rows.sort(key=lambda t: -t[3])
+    return rows
+
+
+def top_k_fusion_candidates_by_dbcv_proxy(
+    items: list[tuple[pathlib.Path, str, str, float]],
+    order: int,
+    k: int,
+) -> list[tuple[list[pathlib.Path], list[str], list[str], float]]:
+    """
+    Up to ``k`` distinct ``order``-tuples of distinct embeddings with highest sum of
+    per-embedding DBCV scores (cheap proxy before running fused clustering).
+
+    Each element is (
+        paths in combination order,
+        models,
+        layers,
+        sum_singleton_scores,
+    ). Component identity is sorted lexicographically by (model, layer).
+    """
+    if order < 2 or k < 1 or len(items) < order:
+        return []
+
+    best: list[tuple[float, tuple[int, ...]]] = []
+    indices = range(len(items))
+    for idxs in combinations(indices, order):
+        s = sum(items[i][3] for i in idxs)
+        best.append((s, idxs))
+    best.sort(key=lambda t: (-t[0], t[1]))
+
+    seen: set[tuple[tuple[str, str], ...]] = set()
+    out: list[tuple[list[pathlib.Path], list[str], list[str], float]] = []
+    for sum_s, idxs in best:
+        components = sorted((items[i][1], items[i][2]) for i in idxs)
+        key = tuple(components)
+        if key in seen:
+            continue
+        seen.add(key)
+        idxs_list = list(idxs)
+        paths = [items[i][0] for i in idxs_list]
+        models = [items[i][1] for i in idxs_list]
+        layers = [items[i][2] for i in idxs_list]
+        out.append((paths, models, layers, sum_s))
+        if len(out) >= k:
+            break
+    return out
diff --git a/pelinker/clustering_grid.py b/pelinker/clustering_grid.py
new file mode 100644
index 0000000..6593388
--- /dev/null
+++ b/pelinker/clustering_grid.py
@@ -0,0 +1,389 @@
+"""HDBSCAN min_cluster_size grid evaluation, cross-sample aggregation, and smooth optimum selection."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Literal
+
+import hdbscan
+import numpy as np
+import pandas as pd
+import torch
+from torch.nn import functional as F
+
+
+@dataclass(frozen=True)
+class ScalarMetricAggregate:
+    """Mean, dispersion, and sample count for one metric at a single grid point."""
+
+    mean: float
+    std: float
+    count: int
+
+
+@dataclass(frozen=True)
+class AggregatedGridPoint:
+    """One grid value of ``min_cluster_size`` with aggregated metrics across samples."""
+
+    min_cluster_size: int
+    dbcv: ScalarMetricAggregate
+    icm_mean: float
+    n_clusters_mean: float
+
+
+@dataclass(frozen=True)
+class AggregatedGridReport:
+    """Typed aggregation of per-sample grid metrics; points are sorted by ``min_cluster_size``."""
+
+    points: tuple[AggregatedGridPoint, ...]
+
+    def __post_init__(self) -> None:
+        sizes = [p.min_cluster_size for p in self.points]
+        if sizes != sorted(sizes):
+            raise ValueError(
+                "AggregatedGridReport.points must be sorted by min_cluster_size"
+            )
+        if len(set(sizes)) != len(sizes):
+            raise ValueError(
+                "Duplicate min_cluster_size in AggregatedGridReport.points"
+            )
+
+
+@dataclass(frozen=True)
+class SmoothedGridOptimumResult:
+    """Diagnostics for ``solve_optimal_min_cluster_size_from_aggregated``."""
+
+    chosen_min_cluster_size: int
+    score_mean_at_chosen: float
+    score_std_at_chosen: float
+    x: tuple[float, ...]
+    y_objective: tuple[float, ...]
+    y_smooth: tuple[float, ...]
+    dy_dx: tuple[float, ...]
+    d2y_dx2: tuple[float, ...]
+    selection: Literal["plateau_derivative", "smoothed_argmax"]
+
+
+def _ensure_odd_window(window: int) -> int:
+    if window < 1:
+        raise ValueError("smooth window must be >= 1")
+    return window if window % 2 == 1 else window + 1
+
+
+def _precision_weights(
+    std: np.ndarray, count: np.ndarray, eps: float = 1e-8
+) -> np.ndarray:
+    """Inverse-variance style weights; uses sample count as an extra multiplier."""
+    var = std * std + eps
+    return count.astype(float) / var
+
+
+def _weighted_centered_moving_average(
+    values: np.ndarray,
+    point_weights: np.ndarray,
+    window: int,
+) -> np.ndarray:
+    """Centered moving average; within each window, averages are weighted by ``point_weights``."""
+    w = _ensure_odd_window(window)
+    half = w // 2
+    n = len(values)
+    out = np.empty(n, dtype=np.float64)
+    for i in range(n):
+        lo = max(0, i - half)
+        hi = min(n, i + half + 1)
+        pw = point_weights[lo:hi]
+        vv = values[lo:hi]
+        denom = float(np.sum(pw))
+        out[i] = float(np.sum(pw * vv) / denom) if denom > 0 else float("nan")
+    return out
+
+
+def _uniform_centered_moving_average(values: np.ndarray, window: int) -> np.ndarray:
+    w = _ensure_odd_window(window)
+    half = w // 2
+    n = len(values)
+    out = np.empty(n, dtype=np.float64)
+    for i in range(n):
+        lo = max(0, i - half)
+        hi = min(n, i + half + 1)
+        out[i] = float(np.mean(values[lo:hi]))
+    return out
+
+
+def _build_objective_y(
+    report: AggregatedGridReport,
+    method: str,
+    uncertainty_penalty: float,
+) -> np.ndarray:
+    means = np.array([p.dbcv.mean for p in report.points], dtype=np.float64)
+    stds = np.array([p.dbcv.std for p in report.points], dtype=np.float64)
+    if method == "mean":
+        return means
+    if method == "lower_bound":
+        return means - uncertainty_penalty * stds
+    if method == "weighted":
+        return means
+    raise ValueError(f"Unknown optimization method: {method!r}")
+
+
+def solve_optimal_min_cluster_size_from_aggregated(
+    report: AggregatedGridReport,
+    *,
+    method: str = "mean",
+    uncertainty_penalty: float = 1.0,
+    smooth_window: int = 3,
+    plateau_fraction: float = 0.92,
+    derivative_rel_tol: float = 0.12,
+    precision_weighted_smooth: bool | None = None,
+) -> SmoothedGridOptimumResult:
+    """
+    Choose ``min_cluster_size`` from aggregated noisy grid scores.
+
+    Builds an objective f(x) from per-point means (optionally mean - penalty·std), smooths f
+    with a centered moving average (uniform or precision-weighted by count / variance), then
+    prefers the **leftmost** x where the smoothed curve is near its plateau
+    (f ≥ ``plateau_fraction`` · max f and |df/dx| small). If none qualify, uses the smoothed
+    maximum.
+
+    ``precision_weighted_smooth`` defaults to True for ``lower_bound`` and ``weighted``,
+    and False for ``mean``.
+    """
+    if len(report.points) == 0:
+        raise ValueError("No aggregated grid points provided")
+
+    x = np.array([p.min_cluster_size for p in report.points], dtype=np.float64)
+    y = _build_objective_y(report, method, uncertainty_penalty)
+    stds = np.array([p.dbcv.std for p in report.points], dtype=np.float64)
+    counts = np.array([p.dbcv.count for p in report.points], dtype=np.float64)
+    dbcv_means = np.array([p.dbcv.mean for p in report.points], dtype=np.float64)
+
+    finite = np.isfinite(x) & np.isfinite(y)
+    if not np.any(finite):
+        raise ValueError("No finite objective values in aggregated grid report")
+
+    x = x[finite]
+    y = y[finite]
+    stds = stds[finite]
+    counts = counts[finite]
+    dbcv_means = dbcv_means[finite]
+    order = np.argsort(x)
+    x = x[order]
+    y = y[order]
+    stds = stds[order]
+    counts = counts[order]
+    dbcv_means = dbcv_means[order]
+
+    if precision_weighted_smooth is None:
+        precision_weighted_smooth = method in ("lower_bound", "weighted")
+
+    if precision_weighted_smooth:
+        pweights = _precision_weights(stds, counts)
+        y_s = _weighted_centered_moving_average(y, pweights, smooth_window)
+    else:
+        y_s = _uniform_centered_moving_average(y, smooth_window)
+
+    dydx = np.gradient(y_s, x)
+    d2ydx2 = np.gradient(dydx, x)
+
+    y_max = float(np.nanmax(y_s))
+    if not np.isfinite(y_max):
+        raise ValueError("Smoothed objective is non-finite")
+
+    abs_dydx = np.abs(dydx)
+    scale = float(np.nanmax(abs_dydx))
+    if not np.isfinite(scale) or scale <= 0:
+        scale = 1.0
+    thresh = derivative_rel_tol * scale
+    level = plateau_fraction * y_max
+
+    chosen_idx: int | None = None
+    selection: Literal["plateau_derivative", "smoothed_argmax"] = "smoothed_argmax"
+    for i in range(len(x)):
+        if not np.isfinite(y_s[i]) or not np.isfinite(dydx[i]):
+            continue
+        if y_s[i] >= level and abs_dydx[i] <= thresh:
+            chosen_idx = i
+            selection = "plateau_derivative"
+            break
+
+    if chosen_idx is None:
+        chosen_idx = int(np.nanargmax(y_s))
+
+    chosen_x = int(x[chosen_idx])
+    score_mean_at = float(dbcv_means[chosen_idx])
+    score_std_at = float(stds[chosen_idx])
+
+    return SmoothedGridOptimumResult(
+        chosen_min_cluster_size=chosen_x,
+        score_mean_at_chosen=score_mean_at,
+        score_std_at_chosen=score_std_at,
+        x=tuple(float(v) for v in x),
+        y_objective=tuple(float(v) for v in y),
+        y_smooth=tuple(float(v) for v in y_s),
+        dy_dx=tuple(float(v) for v in dydx),
+        d2y_dx2=tuple(float(v) for v in d2ydx2),
+        selection=selection,
+    )
+
+
+def aggregated_grid_report_to_dataframe(report: AggregatedGridReport) -> pd.DataFrame:
+    """Lossless round-trip style export for notebooks (typed report → table)."""
+    rows: list[dict[str, float | int]] = []
+    for p in report.points:
+        rows.append(
+            {
+                "min_cluster_size": p.min_cluster_size,
+                "dbcv_mean": p.dbcv.mean,
+                "dbcv_std": p.dbcv.std,
+                "dbcv_count": p.dbcv.count,
+                "icm_mean": p.icm_mean,
+                "n_clusters_mean": p.n_clusters_mean,
+            }
+        )
+    return pd.DataFrame(rows)
+
+
+def cosine_similarity_std(
+    tensor: torch.Tensor, max_pairs: int = 200_000, random_seed: int = 13
+) -> torch.Tensor:
+    """
+    Calculate the standard deviation of pairwise cosine similarities
+    for a tensor of shape (n_b, dim_emb).
+    """
+    normalized = F.normalize(tensor.float(), p=2, dim=1)
+
+    n_points = normalized.size(0)
+    if n_points < 2:
+        return torch.tensor(float("nan"), dtype=normalized.dtype)
+
+    total_pairs = n_points * (n_points - 1) // 2
+
+    if total_pairs <= max_pairs:
+        cos_sim_matrix = torch.mm(normalized, normalized.t())
+        triu_indices = torch.triu_indices(
+            cos_sim_matrix.size(0), cos_sim_matrix.size(1), offset=1
+        )
+        cos_similarities = cos_sim_matrix[triu_indices[0], triu_indices[1]]
+        return torch.std(cos_similarities)
+
+    sample_size = min(max_pairs, total_pairs)
+    generator = torch.Generator(device=normalized.device)
+    generator.manual_seed(random_seed)
+
+    idx_i = torch.randint(
+        0, n_points, (sample_size,), generator=generator, device=normalized.device
+    )
+    idx_j = torch.randint(
+        0, n_points - 1, (sample_size,), generator=generator, device=normalized.device
+    )
+    idx_j = idx_j + (idx_j >= idx_i).long()  # ensure i != j
+    cos_similarities = (normalized[idx_i] * normalized[idx_j]).sum(dim=1)
+    return torch.std(cos_similarities)
+
+
+def evaluate_cluster_size_grid(
+    dfr2: pd.DataFrame,
+    umap_columns: list[str],
+    sizes: list[int],
+    max_pairs_per_cluster: int = 200_000,
+) -> pd.DataFrame:
+    """
+    Evaluate clustering metrics on a grid of min_cluster_size values.
+
+    Uses DBCV (Density-Based Clustering Validation) metric.
+
+    Returns:
+        DataFrame with columns: min_cluster_size, icm, n_clusters, dbcv
+    """
+    umap_values = dfr2[umap_columns].to_numpy(dtype=np.float32, copy=False)
+
+    metrics = []
+    for size in sizes:
+        clusterer = hdbscan.HDBSCAN(min_cluster_size=size, gen_min_span_tree=True)
+        labels = clusterer.fit_predict(umap_values)
+
+        ic = []
+        for ix in np.unique(labels):
+            if ix == -1:
+                continue
+            cluster_values = umap_values[labels == ix]
+            if len(cluster_values) < 2:
+                continue
+            tgroup = torch.from_numpy(cluster_values)
+            st = cosine_similarity_std(
+                tgroup, max_pairs=max_pairs_per_cluster, random_seed=13
+            )
+            ic += [float(st)]
+
+        icm = np.mean(ic) if ic else np.nan
+
+        unique_labels = set(labels)
+        n_clusters = len(unique_labels) - (1 if -1 in unique_labels else 0)
+
+        if n_clusters >= 2:
+            rv = getattr(clusterer, "relative_validity_", None)
+            dbcv = float(rv) if rv is not None else float("nan")
+        else:
+            dbcv = np.nan
+
+        if n_clusters >= 1:
+            metrics += [(size, icm, n_clusters, dbcv)]
+
+    return pd.DataFrame(
+        metrics, columns=["min_cluster_size", "icm", "n_clusters", "dbcv"]
+    )
+
+
+def aggregate_grid_metrics(all_metrics_dfs: list[pd.DataFrame]) -> AggregatedGridReport:
+    """
+    Aggregate grid evaluation metrics across multiple samples into a typed report.
+
+    Per ``min_cluster_size`` we keep DBCV mean, std, and count (so uncertainty is not
+    discarded). ICM and cluster count are aggregated as means for diagnostics.
+    """
+    if not all_metrics_dfs:
+        return AggregatedGridReport(points=())
+
+    combined = pd.concat(all_metrics_dfs, ignore_index=True)
+
+    aggregated = (
+        combined.groupby("min_cluster_size")
+        .agg(
+            {
+                "dbcv": ["mean", "std", "count"],
+                "icm": "mean",
+                "n_clusters": "mean",
+            }
+        )
+        .reset_index()
+    )
+
+    aggregated.columns = [
+        "min_cluster_size",
+        "dbcv_mean",
+        "dbcv_std",
+        "dbcv_count",
+        "icm_mean",
+        "n_clusters_mean",
+    ]
+
+    aggregated["dbcv_std"] = aggregated["dbcv_std"].fillna(0.0)
+    aggregated["dbcv_count"] = aggregated["dbcv_count"].astype(int)
+
+    points: list[AggregatedGridPoint] = []
+    for _, row in aggregated.sort_values("min_cluster_size").iterrows():
+        mcs = int(row["min_cluster_size"])
+        points.append(
+            AggregatedGridPoint(
+                min_cluster_size=mcs,
+                dbcv=ScalarMetricAggregate(
+                    mean=float(row["dbcv_mean"]),
+                    std=float(row["dbcv_std"]),
+                    count=int(row["dbcv_count"]),
+                ),
+                icm_mean=float(row["icm_mean"]),
+                n_clusters_mean=float(row["n_clusters_mean"]),
+            )
+        )
+
+    return AggregatedGridReport(points=tuple(points))
diff --git a/pelinker/clustering_quality_checkpoint.py b/pelinker/clustering_quality_checkpoint.py
new file mode 100644
index 0000000..8cf6aee
--- /dev/null
+++ b/pelinker/clustering_quality_checkpoint.py
@@ -0,0 +1,283 @@
+"""Structured checkpoint I/O for ``run/analysis/clustering_quality.py`` runs."""
+
+from __future__ import annotations
+
+import json
+import pathlib
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from hashlib import sha256
+from typing import Any, Literal
+
+CHECKPOINT_VERSION = 1
+DEFAULT_CHECKPOINT_NAME = "clustering_quality.state.json"
+
+StageState = Literal["pending", "in_progress", "complete", "skipped"]
+RunMode = Literal["single", "fusion2", "fusion3", "all"]
+
+
+@dataclass
+class FailureRecord:
+    """One recorded failure for a combination."""
+
+    combination_key: str
+    error: str
+    at: str
+
+
+@dataclass
+class ClusteringQualityCheckpoint:
+    """On-disk checkpoint for resumable clustering quality runs."""
+
+    version: int = CHECKPOINT_VERSION
+    run_fingerprint: str = ""
+    created_at: str = ""
+    updated_at: str = ""
+    completed_combinations: list[str] = field(default_factory=list)
+    summaries_by_key: dict[str, dict[str, str | float | None]] = field(
+        default_factory=dict
+    )
+    singleton_scores_by_key: dict[str, float] = field(default_factory=dict)
+    stages: dict[str, StageState] = field(default_factory=dict)
+    failures: list[FailureRecord] = field(default_factory=list)
+    # Last --fusion-pairs / --fusion-triples reflected in fusion summaries; if CLI
+    # differs, arity-2/3 checkpoint rows are dropped (singletons keep the same fp).
+    checkpoint_fusion_pairs: int | None = None
+    checkpoint_fusion_triples: int | None = None
+
+    def to_json_dict(self) -> dict[str, Any]:
+        return {
+            "version": self.version,
+            "run_fingerprint": self.run_fingerprint,
+            "created_at": self.created_at,
+            "updated_at": self.updated_at,
+            "checkpoint_fusion_pairs": self.checkpoint_fusion_pairs,
+            "checkpoint_fusion_triples": self.checkpoint_fusion_triples,
+            "completed_combinations": sorted(self.completed_combinations),
+            "summaries_by_key": {
+                k: dict(v) for k, v in sorted(self.summaries_by_key.items())
+            },
+            "singleton_scores_by_key": {
+                k: float(v) for k, v in sorted(self.singleton_scores_by_key.items())
+            },
+            "stages": dict(sorted(self.stages.items())),
+            "failures": [
+                {"combination_key": f.combination_key, "error": f.error, "at": f.at}
+                for f in self.failures
+            ],
+        }
+
+    @staticmethod
+    def from_json_dict(data: dict[str, Any]) -> ClusteringQualityCheckpoint:
+        if int(data.get("version", -1)) != CHECKPOINT_VERSION:
+            raise ValueError(
+                f"Unsupported checkpoint version: {data.get('version')!r}; "
+                f"expected {CHECKPOINT_VERSION}"
+            )
+        failures_raw = data.get("failures") or []
+        failures = [
+            FailureRecord(
+                combination_key=str(fr["combination_key"]),
+                error=str(fr["error"]),
+                at=str(fr["at"]),
+            )
+            for fr in failures_raw
+        ]
+        cfp = data.get("checkpoint_fusion_pairs")
+        cft = data.get("checkpoint_fusion_triples")
+        return ClusteringQualityCheckpoint(
+            version=int(data["version"]),
+            run_fingerprint=str(data["run_fingerprint"]),
+            created_at=str(data.get("created_at", "")),
+            updated_at=str(data.get("updated_at", "")),
+            completed_combinations=list(data.get("completed_combinations") or []),
+            summaries_by_key=dict(data.get("summaries_by_key") or {}),
+            singleton_scores_by_key={
+                str(k): float(v)
+                for k, v in (data.get("singleton_scores_by_key") or {}).items()
+            },
+            stages=dict(data.get("stages") or {}),
+            failures=failures,
+            checkpoint_fusion_pairs=int(cfp) if cfp is not None else None,
+            checkpoint_fusion_triples=int(cft) if cft is not None else None,
+        )
+
+
+def utc_now_iso() -> str:
+    return datetime.now(timezone.utc).replace(microsecond=0).isoformat()
+
+
+def combination_key_from_members(members: list[tuple[str, str]]) -> str:
+    """
+    Canonical key for a combination of (model, layer) embeddings.
+
+    Members are sorted lexicographically by (model, layer). Arity is ``len(members)``.
+    """
+    if not members:
+        raise ValueError("members must be non-empty")
+    arity = len(members)
+    sorted_m = sorted(members, key=lambda t: (t[0], t[1]))
+    inner = "+".join(f"{m}/{layer}" for m, layer in sorted_m)
+    return f"{arity}:{inner}"
+
+
+def compute_run_fingerprint(config: dict[str, Any]) -> str:
+    """Stable SHA256 over a JSON-serializable config (sorted keys)."""
+    blob = json.dumps(config, sort_keys=True, separators=(",", ":")).encode("utf-8")
+    return sha256(blob).hexdigest()
+
+
+def fingerprint_config_from_cli(
+    *,
+    input_dir: pathlib.Path,
+    umap_dim: int,
+    pca_components: int,
+    min_class_size: int,
+    seed: int,
+    frac: float,
+    n_embedding_batches: int | None,
+    batch_size: int,
+    prefix: str,
+    n_sample: int,
+    selected_labels_kb_path: pathlib.Path | None,
+    max_scale: int,
+) -> dict[str, Any]:
+    """Parameters that must match between checkpoint and resume.
+
+    ``--fusion-pairs`` / ``--fusion-triples`` are excluded: they only affect which
+    fusion jobs run; changing them invalidates fusion rows via
+    :func:`reconcile_fusion_checkpoint_params` instead of the run fingerprint.
+
+    ``--mode`` is excluded so you can resume with a different mode (e.g. singletons
+    under ``all``, then ``--mode fusion2``) on the same data fingerprint.
+    """
+    kb = None
+    if selected_labels_kb_path is not None:
+        kb = str(selected_labels_kb_path.expanduser().resolve())
+    return {
+        "batch_size": batch_size,
+        "frac": frac,
+        "n_embedding_batches": n_embedding_batches,
+        "input_dir": str(input_dir.expanduser().resolve()),
+        "max_scale": max_scale,
+        "min_class_size": min_class_size,
+        "n_sample": n_sample,
+        "pca_components": pca_components,
+        "prefix": prefix,
+        "seed": seed,
+        "selected_labels_kb_path": kb,
+        "umap_dim": umap_dim,
+    }
+
+
+def _is_fusion_combination_key(key: str) -> bool:
+    if ":" not in key:
+        return False
+    arity_str, _body = key.split(":", 1)
+    try:
+        arity = int(arity_str)
+    except ValueError:
+        return False
+    return arity >= 2
+
+
+def reconcile_fusion_checkpoint_params(
+    ckpt: ClusteringQualityCheckpoint,
+    *,
+    fusion_pairs: int,
+    fusion_triples: int,
+) -> int:
+    """
+    If fusion CLI counts changed, drop arity >= 2 checkpoint state so fusion is
+    recomputed while singletons stay cacheable.
+
+    Returns how many distinct fusion combination keys were removed from completed /
+    summaries (0 if fusion params already matched this run).
+    """
+    if (
+        ckpt.checkpoint_fusion_pairs == fusion_pairs
+        and ckpt.checkpoint_fusion_triples == fusion_triples
+    ):
+        return 0
+
+    removed_keys = {
+        k for k in ckpt.completed_combinations if _is_fusion_combination_key(k)
+    } | {k for k in ckpt.summaries_by_key if _is_fusion_combination_key(k)}
+
+    ckpt.completed_combinations = [
+        k for k in ckpt.completed_combinations if not _is_fusion_combination_key(k)
+    ]
+    ckpt.summaries_by_key = {
+        k: v
+        for k, v in ckpt.summaries_by_key.items()
+        if not _is_fusion_combination_key(k)
+    }
+    ckpt.failures = [
+        f for f in ckpt.failures if not _is_fusion_combination_key(f.combination_key)
+    ]
+    for name in ("fusion2", "fusion3"):
+        ckpt.stages[name] = "pending"
+    ckpt.checkpoint_fusion_pairs = fusion_pairs
+    ckpt.checkpoint_fusion_triples = fusion_triples
+    return len(removed_keys)
+
+
+def load_checkpoint(path: pathlib.Path) -> ClusteringQualityCheckpoint:
+    raw = path.read_text(encoding="utf-8")
+    data = json.loads(raw)
+    if not isinstance(data, dict):
+        raise ValueError("checkpoint must be a JSON object")
+    return ClusteringQualityCheckpoint.from_json_dict(data)
+
+
+def save_checkpoint_atomic(
+    path: pathlib.Path, checkpoint: ClusteringQualityCheckpoint
+) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    checkpoint.updated_at = utc_now_iso()
+    tmp = path.with_suffix(path.suffix + ".tmp")
+    payload = json.dumps(
+        checkpoint.to_json_dict(), indent=2, sort_keys=True, ensure_ascii=False
+    )
+    tmp.write_text(payload + "\n", encoding="utf-8")
+    tmp.replace(path)
+
+
+def new_checkpoint(fingerprint: str) -> ClusteringQualityCheckpoint:
+    now = utc_now_iso()
+    return ClusteringQualityCheckpoint(
+        version=CHECKPOINT_VERSION,
+        run_fingerprint=fingerprint,
+        created_at=now,
+        updated_at=now,
+        stages={
+            "single": "pending",
+            "fusion2": "pending",
+            "fusion3": "pending",
+        },
+    )
+
+
+def model_layer_from_singleton_key(key: str) -> tuple[str, str]:
+    """Parse ``1:model/layer`` into (model, layer)."""
+    if ":" not in key:
+        raise ValueError(f"invalid combination key: {key!r}")
+    arity_str, body = key.split(":", 1)
+    if int(arity_str) != 1:
+        raise ValueError(f"expected arity 1 key, got {key!r}")
+    if "/" not in body:
+        raise ValueError(f"invalid singleton key body: {body!r}")
+    model, layer = body.split("/", 1)
+    return model, layer
+
+
+def score_by_model_layer_from_checkpoint(
+    singleton_scores_by_key: dict[str, float],
+) -> dict[tuple[str, str], float]:
+    out: dict[tuple[str, str], float] = {}
+    for k, score in singleton_scores_by_key.items():
+        if not k.startswith("1:"):
+            continue
+        ml = model_layer_from_singleton_key(k)
+        out[ml] = float(score)
+    return out
diff --git a/pelinker/conf/__init__.py b/pelinker/conf/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pelinker/conf/fit.yaml b/pelinker/conf/fit.yaml
new file mode 100644
index 0000000..3171619
--- /dev/null
+++ b/pelinker/conf/fit.yaml
@@ -0,0 +1,6 @@
+defaults:
+-   fit_config
+-   _self_
+
+hydra:
+    output_subdir:
diff --git a/pelinker/conf/server.yaml b/pelinker/conf/server.yaml
new file mode 100644
index 0000000..bdbd4c0
--- /dev/null
+++ b/pelinker/conf/server.yaml
@@ -0,0 +1,6 @@
+defaults:
+-   server_config
+-   _self_
+
+hydra:
+    output_subdir:
diff --git a/pelinker/config.py b/pelinker/config.py
new file mode 100644
index 0000000..8647ea1
--- /dev/null
+++ b/pelinker/config.py
@@ -0,0 +1,190 @@
+from __future__ import annotations
+
+import os
+from dataclasses import asdict, dataclass, field
+from datetime import date
+from pathlib import Path
+from typing import Any
+
+from numpy.random import RandomState
+
+
+def _validate_semver(version: str) -> None:
+    """Require semver 2.0.0 core MAJOR.MINOR.PATCH; allow optional -prerelease+build."""
+    s = version.strip()
+    if not s:
+        raise ValueError("version must be a non-empty string")
+    if "+" in s:
+        s = s.split("+", 1)[0]
+    if "-" in s:
+        s = s.split("-", 1)[0]
+    parts = s.split(".")
+    if len(parts) != 3:
+        raise ValueError(
+            f"version core must be semver MAJOR.MINOR.PATCH, got {version!r}"
+        )
+    for p in parts:
+        if not p.isdigit():
+            raise ValueError(f"invalid semver numeric segment {p!r} in {version!r}")
+        n = int(p)
+        if p != "0" and p != str(n):
+            raise ValueError(
+                f"semver numeric segments must not have leading zeros: {version!r}"
+            )
+
+
+@dataclass(frozen=True)
+class KBConfig:
+    """Metadata for the knowledge base packaged with a fitted Linker."""
+
+    name: str
+    version: str
+    created_at: date
+    description: str = ""
+    entity_count: int | None = None
+    """Set after fit from vocabulary size when None at construction time."""
+
+    def __post_init__(self) -> None:
+        if not self.name.strip():
+            raise ValueError("name must be a non-empty string")
+        _validate_semver(self.version)
+        if self.entity_count is not None and self.entity_count < 0:
+            raise ValueError("entity_count must be >= 0 when provided")
+
+
+@dataclass(frozen=True)
+class EmbeddingSourceSpec:
+    """One backbone + layer selection (e.g. for a single encoder or one branch of a fused model)."""
+
+    model_type: str
+    layers_spec: str
+
+    def __post_init__(self) -> None:
+        if not self.model_type:
+            raise ValueError("model_type must be a non-empty string")
+        if not self.layers_spec:
+            raise ValueError("layers_spec must be a non-empty string")
+
+
+@dataclass(frozen=True)
+class EmbeddingModelMetadata:
+    """Describes which embedding backbones/layers produced the model (saved with the Linker)."""
+
+    sources: tuple[EmbeddingSourceSpec, ...]
+
+    def __post_init__(self) -> None:
+        if not self.sources:
+            raise ValueError("sources must contain at least one EmbeddingSourceSpec")
+
+    @classmethod
+    def from_single(cls, model_type: str, layers_spec: str) -> EmbeddingModelMetadata:
+        return cls(
+            sources=(
+                EmbeddingSourceSpec(model_type=model_type, layers_spec=layers_spec),
+            )
+        )
+
+
+@dataclass
+class EmbeddingTrainingConfig:
+    """Inputs and runtime settings used only while embedding the corpus (not part of model identity)."""
+
+    input_text_table_path: Path
+    kb_csv_path: Path
+    use_gpu: bool = False
+    input_buffer_rows: int = 1000
+    """Rows read per ``pandas.read_csv(..., chunksize=...)`` pass over the text table (I/O buffer only)."""
+    encoder_batch_size: int = 200
+    """How many table rows are encoded per transformer forward pass; lower if GPU memory is tight."""
+    nlp_model: str = "en_core_web_trf"
+    max_input_buffers: int | None = None
+    """If set, stop after this many text-table read passes (each up to ``input_buffer_rows`` rows)."""
+
+    def __post_init__(self) -> None:
+        if self.input_buffer_rows < 1:
+            raise ValueError("input_buffer_rows must be >= 1")
+        if self.encoder_batch_size < 1:
+            raise ValueError("encoder_batch_size must be >= 1")
+        if self.max_input_buffers is not None and self.max_input_buffers < 1:
+            raise ValueError("max_input_buffers must be >= 1 when provided")
+        self.input_text_table_path = Path(
+            os.path.expandvars(os.fspath(self.input_text_table_path))
+        ).expanduser()
+        self.kb_csv_path = Path(
+            os.path.expandvars(os.fspath(self.kb_csv_path))
+        ).expanduser()
+
+
+@dataclass
+class ClusteringOptimizationConfig:
+    """Configuration for clustering optimization grid search."""
+
+    min_class_size: int = 20
+    max_scale: int = 100
+    rns: RandomState = field(default_factory=lambda: RandomState(seed=13))
+    frac: float = 1.0
+    n_embedding_batches: int | None = None
+    """Cap parquet reads at this many batches (`batch_size` rows each); None = read all."""
+    batch_size: int = 1000
+    """Rows per batch when **reading mention-level embedding parquet** (not encoder batch size)."""
+    optimization_method: str = "mean"
+    """How to build the objective f(min_cluster_size) before smoothing (mean / lower_bound / weighted)."""
+    grid_smooth_window: int = 3
+    """Odd-length centered moving-average window for smoothing f(x). Even values are bumped up by one."""
+    grid_plateau_fraction: float = 0.92
+    """Require smoothed f(x) >= this fraction of the smoothed maximum to accept a plateau point."""
+    grid_derivative_rel_tol: float = 0.12
+    """|df/dx| below this times max|df/dx| counts as “derivative near zero” on the smoothed curve."""
+
+    def __post_init__(self) -> None:
+        if self.min_class_size < 1:
+            raise ValueError("min_class_size must be >= 1")
+        if self.max_scale < self.min_class_size:
+            raise ValueError("max_scale must be >= min_class_size")
+        if self.batch_size < 1:
+            raise ValueError("batch_size must be >= 1")
+        if not 0 < self.frac <= 1:
+            raise ValueError("frac must be in range (0, 1]")
+        if self.n_embedding_batches is not None and self.n_embedding_batches < 1:
+            raise ValueError("n_embedding_batches must be >= 1 when provided")
+        if not self.optimization_method:
+            raise ValueError("optimization_method must be a non-empty string")
+        if self.grid_smooth_window < 1:
+            raise ValueError("grid_smooth_window must be >= 1")
+        if not 0 < self.grid_plateau_fraction <= 1:
+            raise ValueError("grid_plateau_fraction must be in (0, 1]")
+        if self.grid_derivative_rel_tol <= 0:
+            raise ValueError("grid_derivative_rel_tol must be > 0")
+
+    def to_dict(self) -> dict[str, Any]:
+        return asdict(self)
+
+
+@dataclass
+class TransformConfig:
+    """Configuration for the embedding transformation pipeline."""
+
+    # PCA configuration
+    pca_components: int = 50
+    """Number of principal components to keep after PCA reduction."""
+
+    # UMAP configuration
+    umap_components: int = 4
+    """Number of UMAP dimensions for clustering (typically 3-5)."""
+    umap_metric: str = "cosine"
+    """Distance metric for UMAP (default: 'cosine')."""
+
+    # Visualization UMAP configuration
+    umap_viz_components: int = 3
+    """Number of UMAP dimensions for visualization (default: 3)."""
+    umap_viz_metric: str = "cosine"
+    """Distance metric for visualization UMAP (default: 'cosine')."""
+
+    def __post_init__(self):
+        """Validate configuration parameters."""
+        if self.pca_components < 1:
+            raise ValueError("pca_components must be >= 1")
+        if self.umap_components < 2:
+            raise ValueError("umap_components must be >= 2")
+        if self.umap_viz_components < 2:
+            raise ValueError("umap_viz_components must be >= 2")
diff --git a/pelinker/embedder.py b/pelinker/embedder.py
index 3e9a864..95027d3 100644
--- a/pelinker/embedder.py
+++ b/pelinker/embedder.py
@@ -1,12 +1,28 @@
+import os
 import pathlib
+from collections.abc import Sequence
+
+import logging
+
+import pandas as pd
 import spacy
 import torch
-import pandas as pd
-import tqdm
-import logging
+from rich.console import Console
+from rich.progress import (
+    BarColumn,
+    Progress,
+    SpinnerColumn,
+    TextColumn,
+    TimeElapsedColumn,
+)
 
+from pelinker.config import (
+    EmbeddingModelMetadata,
+    EmbeddingSourceSpec,
+    EmbeddingTrainingConfig,
+)
 from pelinker.ops import _detect_file_format, _detect_headers_and_columns
-from pelinker.util import load_models, extract_and_embed_mentions
+from pelinker.util import expand_config_path, extract_and_embed_mentions, load_models
 from pelinker.onto import WordGrouping
 from pelinker.util import str2layers
 from pelinker.io.parquet import ParquetWriter
@@ -14,143 +30,275 @@
 logger = logging.getLogger(__name__)
 
 
-def embed_kb_corpus(
-    model_type: str,
-    layers_spec: str,
-    input_text_table_path: pathlib.Path,
-    kb_csv_path: pathlib.Path,
-    output_parquet_path: pathlib.Path,
-    use_gpu: bool = False,
-    chunk_size: int = 1000,
-    batch_size: int = 200,
-    nlp_model: str = "en_core_web_trf",
-    head: int | None = None,
-):
-    """
-    Embed a knowledge base corpus by processing text data and extracting mentions.
+def _progress_disabled() -> bool:
+    """Set ``PELINKER_NO_PROGRESS=1`` to turn off the embed progress bar (e.g. CI logs)."""
+    return os.environ.get("PELINKER_NO_PROGRESS", "").strip().lower() in (
+        "1",
+        "true",
+        "yes",
+    )
 
-    Args:
-        model_type: Backbone model type for token embeddings (e.g., "biobert")
-        layers_spec: String of layers; e.g., `1,2,3` corresponds to [-1,-2,-3]
-        input_text_table_path: Input file (TSV/CSV, optionally gzipped) with pmid and text columns
-        kb_csv_path: Path to csv file containing "entity_id" and "label" columns, where label is a property form
-        output_parquet_path: Output Parquet file to append to
-        use_gpu: Enable GPU acceleration if CUDA is available
-        chunk_size: Chunk size for streaming input. Each chunk is split into batches and serialized
-        batch_size: Embedding batch size inside a chunk
-        nlp_model: spaCy model to use for tokenization/lemmas
-        head: Number of chunks to process (None for all chunks)
-    """
-    if use_gpu:
-        if torch.cuda.is_available():
-            logger.info("Using GPU in upcoming processes")
-        else:
-            logger.warning("CUDA is not available. Running on CPU instead")
-            use_gpu = False
 
-    # Load models
-    logger.info(f"Loading models: {model_type} and {nlp_model}")
-    tokenizer, model = load_models(model_type, sentence=False)
+def _embed_corpus_single_source(
+    *,
+    source: EmbeddingSourceSpec,
+    training: EmbeddingTrainingConfig,
+    output_parquet_path: pathlib.Path,
+    use_gpu: bool,
+) -> None:
+    """Run one encoder over the corpus and append rows to a single parquet file."""
+    logger.info(
+        "Embedding source model_type=%r layers_spec=%r",
+        source.model_type,
+        source.layers_spec,
+    )
+
+    tokenizer, model = load_models(source.model_type, sentence=False)
     if use_gpu:
         model.to("cuda")
         spacy.require_gpu()
-    nlp = spacy.load(nlp_model)
-    layers = str2layers(layers_spec)
 
-    df_props = pd.read_csv(kb_csv_path)
+    nlp = spacy.load(training.nlp_model)
+    layers = str2layers(source.layers_spec)
+
+    df_props = pd.read_csv(training.kb_csv_path)
     entities = df_props["label"].tolist()
 
-    logger.info(f"Loaded {df_props.shape[0]} properties")
-    logger.info(f"Layers are set to {layers}")
+    logger.info("Loaded %s properties", df_props.shape[0])
+    logger.info("Layers are set to %s", layers)
 
-    # Detect file format and headers
-    file_format = _detect_file_format(input_text_table_path)
+    file_format = _detect_file_format(training.input_text_table_path)
     has_header, pmid_col, text_col = _detect_headers_and_columns(
-        input_text_table_path, file_format
+        training.input_text_table_path, file_format
     )
 
-    logger.info(f"Detected file format: {file_format.upper()}")
-    logger.info(f"Headers detected: {has_header}")
-    logger.info(f"Using columns: '{pmid_col}', {text_col}'")
+    logger.info("Detected file format: %s", file_format.upper())
+    logger.info("Headers detected: %s", has_header)
+    logger.info("Using columns: %r, %r", pmid_col, text_col)
 
-    # Log head parameter usage
-    if head is not None:
-        logger.info(f"Processing only first {head} chunks (head={head})")
+    if training.max_input_buffers is not None:
+        logger.info(
+            "Processing only first %s text-table read passes (max_input_buffers=%s)",
+            training.max_input_buffers,
+            training.max_input_buffers,
+        )
     else:
-        logger.info("Processing all chunks")
+        logger.info("Processing full text table (all read passes)")
 
-    # Set up input reader with detected format and headers
-    compression = "gzip" if input_text_table_path.suffix.endswith(".gz") else None
+    compression = (
+        "gzip" if training.input_text_table_path.suffix.endswith(".gz") else None
+    )
     sep = "\t" if file_format == "tsv" else ","
 
     reader = pd.read_csv(
-        input_text_table_path,
+        training.input_text_table_path,
         sep=sep,
         header=0 if has_header else None,
         compression=compression,
-        chunksize=chunk_size,
+        chunksize=training.input_buffer_rows,
     )
 
-    # Create output directory
-    output_parquet_path = output_parquet_path.expanduser()
+    expanded_out = expand_config_path(output_parquet_path)
+    if expanded_out is None:
+        raise ValueError("output_parquet_path resolved to None")
+    output_parquet_path = pathlib.Path(expanded_out)
     output_parquet_path.parent.mkdir(parents=True, exist_ok=True)
-
-    # Initialize Parquet writer
     parquet_writer = ParquetWriter(output_parquet_path)
 
+    # Rich defaults to "no animations" when isatty() is false (Hydra, IDE runners).
+    # force_terminal=True keeps the bar working there; use PELINKER_NO_PROGRESS=1 to
+    # disable (clean logs when stderr is a file).
+    show_progress = not _progress_disabled()
+    console = Console(
+        stderr=True,
+        width=120,
+        legacy_windows=False,
+        force_terminal=show_progress,
+    )
+
     try:
         total_processed = 0
+        cumulative_mentions = 0
 
-        for i, chunk in tqdm.tqdm(
-            enumerate(reader), desc="Processing chunks", leave=True, position=0
-        ):
-            # Apply head limit if specified
-            if head is not None and i >= head:
-                logger.info(f"Reached head limit of {head} chunks, stopping")
-                break
-            try:
-                # Extract texts and pmids using detected column names
-                chunk_texts = list(chunk[text_col])
-                pmids = [
-                    str(pmid) for pmid in chunk[pmid_col]
-                ]  # Convert PMIDs to strings
-
-                # Extract mentions and embeddings
-                rows_data = extract_and_embed_mentions(
-                    entities=entities,
-                    data=chunk_texts,
-                    batch_size=batch_size,
-                    pmids=pmids,
-                    tokenizer=tokenizer,
-                    model=model,
-                    nlp=nlp,
-                    layers=layers,
-                    word_modes=(WordGrouping.W1, WordGrouping.W2, WordGrouping.W3),
+        with Progress(
+            SpinnerColumn(),
+            TextColumn("[bold blue]{task.description}"),
+            BarColumn(),
+            TextColumn("[progress.percentage]{task.percentage:>3.0f}%"),
+            TimeElapsedColumn(),
+            console=console,
+            refresh_per_second=8,
+            transient=False,
+            disable=not show_progress,
+        ) as progress:
+            max_buf = training.max_input_buffers
+            if max_buf is not None:
+                task_id = progress.add_task(
+                    "[cyan]Embed[/cyan] · starting…",
+                    total=float(max_buf),
+                )
+            else:
+                task_id = progress.add_task(
+                    "[cyan]Embed[/cyan] · starting…",
+                    total=None,
                 )
 
-                # Write to Parquet
+            bs = training.encoder_batch_size
+
+            for i, chunk in enumerate(reader):
+                if max_buf is not None and i >= max_buf:
+                    logger.info(
+                        "Reached max_input_buffers=%s table read passes, stopping",
+                        max_buf,
+                    )
+                    break
+
+                chunk_texts = list(chunk[text_col])
+                pmids = [str(pmid) for pmid in chunk[pmid_col]]
+                buf_label = f"{i + 1}/{max_buf}" if max_buf is not None else str(i + 1)
+                n_enc = (len(chunk_texts) + bs - 1) // bs if chunk_texts else 0
+
+                def on_encoder_batch(
+                    ibatch: int,
+                    n_enc_batches: int,
+                    n_rows_in_buffer: int,
+                ) -> None:
+                    texts_in_buf = min((ibatch + 1) * bs, len(chunk_texts))
+                    texts_cum = total_processed + texts_in_buf
+                    mentions_cum = cumulative_mentions + n_rows_in_buffer
+                    desc = (
+                        f"[cyan]Embed[/cyan] · buf {buf_label} · "
+                        f"encoder batch {ibatch + 1}/{n_enc_batches} · "
+                        f"texts {texts_cum} · mentions {mentions_cum}"
+                    )
+                    if max_buf is not None and n_enc_batches:
+                        progress.update(
+                            task_id,
+                            completed=float(i) + (ibatch + 1) / n_enc_batches,
+                            description=desc,
+                        )
+                    else:
+                        progress.update(task_id, description=desc)
+
+                if n_enc == 0:
+                    rows_data = []
+                    if max_buf is not None:
+                        progress.update(
+                            task_id,
+                            completed=float(i + 1),
+                            description=(
+                                f"[cyan]Embed[/cyan] · buf {buf_label} · "
+                                f"encoder batch 0/0 · texts {total_processed} · "
+                                f"mentions {cumulative_mentions}"
+                            ),
+                        )
+                    else:
+                        progress.update(
+                            task_id,
+                            description=(
+                                f"[cyan]Embed[/cyan] · buf {buf_label} · "
+                                f"encoder batch 0/0 · texts {total_processed} · "
+                                f"mentions {cumulative_mentions}"
+                            ),
+                        )
+                else:
+                    rows_data = extract_and_embed_mentions(
+                        entities=entities,
+                        data=chunk_texts,
+                        batch_size=bs,
+                        pmids=pmids,
+                        tokenizer=tokenizer,
+                        model=model,
+                        nlp=nlp,
+                        layers=layers,
+                        word_modes=(
+                            WordGrouping.W1,
+                            WordGrouping.W2,
+                            WordGrouping.W3,
+                        ),
+                        on_encoder_batch=on_encoder_batch,
+                    )
+
                 if rows_data:
                     parquet_writer.write_batch(rows_data)
 
                 total_processed += len(chunk_texts)
-                logger.info(
-                    f"Chunk {i} completed. Processed {len(chunk_texts)} texts, "
-                    f"extracted {len(rows_data)} mentions. "
-                    f"Total texts processed: {total_processed}"
+                cumulative_mentions += len(rows_data)
+                logger.debug(
+                    "Buffer %s: processed %s texts (%s mentions this buffer); running total texts: %s",
+                    i,
+                    len(chunk_texts),
+                    len(rows_data),
+                    total_processed,
                 )
 
                 del rows_data
 
-            except Exception as e:
-                logger.error(f"Error processing chunk {i}: {e}")
-                raise
+            if max_buf is not None:
+                task_state = progress.tasks[task_id]
+                if 0.0 < task_state.completed < (task_state.total or 0.0):
+                    progress.update(task_id, total=task_state.completed)
 
-    except KeyboardInterrupt:
-        logger.info("Process interrupted by user")
-    except Exception as e:
-        logger.error(f"Unexpected error: {e}")
-        raise
     finally:
-        # Always close the writer
         parquet_writer.close()
-        logger.info(f"Processing completed. Output saved to {output_parquet_path}")
+        logger.info("Output saved to %s", output_parquet_path)
+
+
+def embed_kb_corpus(
+    *,
+    metadata: EmbeddingModelMetadata,
+    training: EmbeddingTrainingConfig,
+    output_parquet_path: pathlib.Path | None = None,
+    output_parquet_paths: Sequence[pathlib.Path] | None = None,
+) -> None:
+    """
+    Embed a knowledge base corpus by processing text data and extracting mentions.
+
+    For ``len(metadata.sources) > 1``, pass ``output_parquet_paths`` with one path per
+    source (same order as ``metadata.sources``). For a single source, pass
+    ``output_parquet_path``.
+    """
+    k = len(metadata.sources)
+    if k == 1:
+        if output_parquet_path is None:
+            raise ValueError(
+                "output_parquet_path is required for a single embedding source"
+            )
+        ope = expand_config_path(output_parquet_path)
+        if ope is None:
+            raise ValueError("output_parquet_path resolved to None")
+        paths: list[pathlib.Path] = [pathlib.Path(ope)]
+    else:
+        if output_parquet_paths is None:
+            raise ValueError(
+                "output_parquet_paths is required when metadata has multiple sources "
+                f"({k}); provide one output path per source in metadata order."
+            )
+        paths = []
+        for p in output_parquet_paths:
+            ep = expand_config_path(p)
+            if ep is None:
+                raise ValueError("output_parquet_paths must not contain None")
+            paths.append(pathlib.Path(ep))
+        if len(paths) != k:
+            raise ValueError(
+                f"output_parquet_paths must have length {k} (metadata.sources), got {len(paths)}"
+            )
+
+    use_gpu = training.use_gpu
+    if use_gpu:
+        if torch.cuda.is_available():
+            logger.info("Using GPU in upcoming processes")
+        else:
+            logger.warning("CUDA is not available. Running on CPU instead")
+            use_gpu = False
+
+    for spec, out_path in zip(metadata.sources, paths):
+        _embed_corpus_single_source(
+            source=spec,
+            training=training,
+            output_parquet_path=out_path,
+            use_gpu=use_gpu,
+        )
+
+    logger.info("All %s embedding source(s) written.", k)
diff --git a/pelinker/embedding_fusion.py b/pelinker/embedding_fusion.py
new file mode 100644
index 0000000..877693d
--- /dev/null
+++ b/pelinker/embedding_fusion.py
@@ -0,0 +1,485 @@
+"""
+Join and concatenate embeddings from multiple parquet sources (same KB export schema).
+
+Mention-level fusion joins on (pmid, property, mention) and concatenates embed vectors
+in source order. Property-level fusion averages mentions within each file per property,
+takes the intersection of properties across sources, and concatenates those means.
+
+``Linker.predict`` fuses mention tensors across sources in metadata order; when
+``model_type`` differs between sources, span alignment is not guaranteed—use the same
+backbone for all sources if order-sensitive fusion is required.
+"""
+
+from __future__ import annotations
+
+import logging
+import pathlib
+from collections.abc import Callable, Mapping, Sequence
+
+import numpy as np
+import pandas as pd
+import pyarrow.parquet as pq
+
+from pelinker.io import read_batches
+
+logger = logging.getLogger(__name__)
+
+
+def _parquet_capped_batch_count(
+    path: pathlib.Path,
+    batch_size: int,
+    n_embedding_batches: int | None,
+) -> int | None:
+    """Return expected batch count for progress, or None if metadata is unavailable."""
+    try:
+        file = pq.ParquetFile(path.as_posix())
+        rows = int(file.metadata.num_rows)
+    except Exception:
+        return None
+    if rows <= 0:
+        return None
+    full = (rows + batch_size - 1) // batch_size
+    if n_embedding_batches is not None:
+        return min(full, n_embedding_batches)
+    return full
+
+
+def _should_emit_read_progress(batch_index: int, total_batches: int | None) -> bool:
+    if total_batches is None or total_batches <= 1:
+        return True
+    step = max(1, total_batches // 50)
+    return batch_index == 1 or batch_index % step == 0 or batch_index >= total_batches
+
+
+def _maybe_emit_read_line(
+    path: pathlib.Path,
+    batch_idx: int,
+    total_batches: int | None,
+    status_fn: Callable[[str], None] | None,
+) -> None:
+    if status_fn is None:
+        return
+    if not _should_emit_read_progress(batch_idx, total_batches):
+        return
+    if total_batches is not None:
+        status_fn(f"embeddings {path.name}: batch {batch_idx}/{total_batches}")
+    else:
+        status_fn(f"embeddings {path.name}: batch {batch_idx}")
+
+
+def _for_each_embedding_parquet_batch(
+    path: pathlib.Path,
+    batch_size: int,
+    n_embedding_batches: int | None,
+    *,
+    read_status: Callable[[str], None] | None,
+    show_read_progress: bool,
+    on_batch: Callable[[int, pd.DataFrame], None],
+) -> None:
+    """Iterate parquet row batches; optional outer status or standalone Rich progress."""
+    total_batches = _parquet_capped_batch_count(path, batch_size, n_embedding_batches)
+
+    def _run(status_fn: Callable[[str], None] | None) -> None:
+        for i, batch in enumerate(read_batches(path.as_posix(), batch_size=batch_size)):
+            on_batch(i, batch)
+            _maybe_emit_read_line(path, i + 1, total_batches, status_fn)
+            if n_embedding_batches is not None and i >= n_embedding_batches - 1:
+                break
+
+    if read_status is not None:
+        _run(read_status)
+    elif show_read_progress:
+        from rich.console import Console
+        from rich.progress import (
+            BarColumn,
+            Progress,
+            SpinnerColumn,
+            TextColumn,
+            TimeElapsedColumn,
+        )
+
+        with Progress(
+            SpinnerColumn(),
+            TextColumn("{task.description}"),
+            BarColumn(),
+            TextColumn("[progress.percentage]{task.percentage:>3.0f}%"),
+            TimeElapsedColumn(),
+            console=Console(),
+            transient=True,
+            refresh_per_second=4,
+        ) as progress:
+            tid = progress.add_task(
+                f"Reading {path.name}",
+                total=total_batches,
+            )
+
+            def _bar_status(msg: str) -> None:
+                if total_batches is not None:
+                    try:
+                        part = msg.split("batch ", 1)[1]
+                        num = int(part.split("/", 1)[0])
+                    except (IndexError, ValueError):
+                        num = None
+                    if num is not None:
+                        progress.update(tid, description=msg, completed=num)
+                        return
+                progress.update(tid, description=msg)
+
+            _run(_bar_status)
+    else:
+        _run(None)
+
+
+JOIN_KEYS: tuple[str, str, str] = ("pmid", "property", "mention")
+
+REQUIRED_MENTION_COLUMNS: frozenset[str] = frozenset(
+    {"pmid", "property", "mention", "embed"}
+)
+
+
+def _to_embed_array(v: list | np.ndarray) -> np.ndarray:
+    arr = np.asarray(v, dtype=np.float64)
+    return arr.reshape(-1) if arr.ndim > 1 and arr.shape[0] == 1 else arr.squeeze()
+
+
+def dedupe_mean_embed_by_keys(
+    df: pd.DataFrame, *, keys: Sequence[str] = JOIN_KEYS
+) -> pd.DataFrame:
+    """
+    Collapse duplicate rows sharing the same join keys by averaging embed vectors.
+    """
+    missing = [c for c in keys if c not in df.columns]
+    if missing:
+        raise ValueError(f"DataFrame missing columns: {missing}")
+
+    def _mean_group(embed_series: pd.Series) -> np.ndarray:
+        vecs = np.stack([_to_embed_array(x) for x in embed_series.values])
+        return np.mean(vecs, axis=0)
+
+    grouped = (
+        df.groupby(list(keys), sort=False)
+        .agg(embed=("embed", _mean_group))
+        .reset_index()
+    )
+    return grouped
+
+
+def mention_level_concat_frames(dfs: Sequence[pd.DataFrame]) -> pd.DataFrame:
+    """
+    Inner-join mention rows across sources and set ``embed`` to concatenated vectors.
+
+    Duplicate keys within a single source are averaged before the join.
+    Concatenation order matches ``dfs`` index order (must match metadata.sources).
+    """
+    if len(dfs) == 0:
+        raise ValueError("mention_level_concat_frames requires at least one frame")
+    prepared: list[pd.DataFrame] = []
+    for i, df in enumerate(dfs):
+        cols = set(df.columns)
+        if not REQUIRED_MENTION_COLUMNS.issubset(cols):
+            missing = REQUIRED_MENTION_COLUMNS - cols
+            raise ValueError(f"Frame {i} missing columns: {sorted(missing)}")
+        sub = df[list(JOIN_KEYS) + ["embed"]].copy()
+        sub = dedupe_mean_embed_by_keys(sub)
+        sub = sub.rename(columns={"embed": f"_e{i}"})
+        prepared.append(sub)
+
+    out = prepared[0]
+    for i in range(1, len(prepared)):
+        out = out.merge(prepared[i], on=list(JOIN_KEYS), how="inner")
+        if len(out) == 0:
+            break
+
+    emb_cols = [f"_e{i}" for i in range(len(dfs))]
+    if len(out) == 0:
+        return pd.DataFrame(columns=list(JOIN_KEYS) + ["embed"])
+
+    if len(dfs) == 1:
+        return out.rename(columns={"_e0": "embed"})
+
+    def _concat_row(row: pd.Series) -> np.ndarray:
+        parts = [np.asarray(row[c], dtype=np.float32) for c in emb_cols]
+        return np.concatenate(parts, axis=0)
+
+    out = out.copy()
+    out["embed"] = [_concat_row(out.iloc[i]) for i in range(len(out))]
+    out = out.drop(columns=emb_cols)
+    return out
+
+
+def read_parquet_to_dataframe(path: pathlib.Path) -> pd.DataFrame:
+    table = pq.read_table(path)
+    return table.to_pandas()
+
+
+def read_embedding_parquet_batches_concat(
+    path: pathlib.Path,
+    *,
+    batch_size: int,
+    n_embedding_batches: int | None = None,
+    read_status: Callable[[str], None] | None = None,
+    show_read_progress: bool = False,
+) -> pd.DataFrame | None:
+    """
+    Stream mention-level embedding parquet via ``read_batches`` and concatenate batches.
+
+    Same batching contract as ``ClusteringOptimizationConfig`` (``batch_size`` rows per
+    batch; optional cap on batch count).
+
+    If ``read_status`` is set, it receives short status lines (for combining with an
+    outer Rich progress bar). If it is omitted and ``show_read_progress`` is True, a
+    compact transient progress display is used for this read only.
+    """
+    if not path.exists():
+        return None
+    agg: list[pd.DataFrame] = []
+    try:
+
+        def on_batch(_i: int, batch: pd.DataFrame) -> None:
+            agg.append(batch)
+
+        _for_each_embedding_parquet_batch(
+            path,
+            batch_size,
+            n_embedding_batches,
+            read_status=read_status,
+            show_read_progress=show_read_progress,
+            on_batch=on_batch,
+        )
+    except Exception:
+        return None
+    if not agg:
+        return None
+    return pd.concat(agg, ignore_index=True)
+
+
+def concat_mention_level_embedding_sources(
+    paths: Sequence[pathlib.Path],
+    *,
+    batch_size: int,
+    n_embedding_batches: int | None = None,
+    read_status: Callable[[str], None] | None = None,
+    show_read_progress: bool = False,
+) -> pd.DataFrame | None:
+    """
+    Load one or more mention-level parquet sources like ``estimate_model_clustering``:
+    read each path in batches, optionally inner-join across sources, return one frame
+    (no ``frac`` sampling).
+    """
+    if len(paths) == 0:
+        return None
+    parts: list[pd.DataFrame] = []
+    n_paths = len(paths)
+    for pi, p in enumerate(paths):
+        prefix = f"[{pi + 1}/{n_paths}] " if n_paths > 1 else ""
+
+        def path_read(msg: str, _pfx: str = prefix) -> None:
+            if read_status is not None:
+                read_status(_pfx + msg)
+
+        part = read_embedding_parquet_batches_concat(
+            p,
+            batch_size=batch_size,
+            n_embedding_batches=n_embedding_batches,
+            read_status=path_read if read_status is not None else None,
+            show_read_progress=show_read_progress and read_status is None,
+        )
+        if part is None or len(part) == 0:
+            return None
+        parts.append(part)
+    if len(parts) == 1:
+        frame = parts[0]
+    else:
+        try:
+            frame = mention_level_concat_frames(parts)
+        except Exception:
+            return None
+    if len(frame) == 0:
+        return None
+    return frame
+
+
+def _accumulate_property_means_from_frame(
+    sums: dict[str, np.ndarray],
+    counts: dict[str, int],
+    df: pd.DataFrame,
+    kb_labels: set[str] | None,
+    *,
+    embed_column: str = "embed",
+    property_column: str = "property",
+) -> None:
+    if property_column not in df.columns or embed_column not in df.columns:
+        return
+    if kb_labels is not None:
+        work = df.loc[df[property_column].isin(kb_labels)]
+    else:
+        work = df
+    if len(work) == 0:
+        return
+    for prop_label, group in work.groupby(property_column, sort=False):
+        key = str(prop_label)
+        stacked = np.stack(
+            [
+                np.asarray(x, dtype=np.float64).reshape(-1)
+                for x in group[embed_column].values
+            ]
+        )
+        batch_sum = stacked.sum(axis=0)
+        batch_count = int(stacked.shape[0])
+        if key in sums:
+            sums[key] += batch_sum
+            counts[key] += batch_count
+        else:
+            sums[key] = batch_sum
+            counts[key] = batch_count
+
+
+def property_mean_vectors_from_parquet_batches(
+    path: pathlib.Path,
+    kb_labels: set[str] | None,
+    *,
+    batch_size: int,
+    n_embedding_batches: int | None = None,
+    read_status: Callable[[str], None] | None = None,
+    show_read_progress: bool = False,
+) -> dict[str, np.ndarray]:
+    """Mean embedding per property by streaming parquet batches (no full-file read)."""
+    sums: dict[str, np.ndarray] = {}
+    counts: dict[str, int] = {}
+
+    def on_batch(_i: int, batch: pd.DataFrame) -> None:
+        _accumulate_property_means_from_frame(sums, counts, batch, kb_labels)
+
+    _for_each_embedding_parquet_batch(
+        path,
+        batch_size,
+        n_embedding_batches,
+        read_status=read_status,
+        show_read_progress=show_read_progress,
+        on_batch=on_batch,
+    )
+    return {k: sums[k] / counts[k] for k in sums}
+
+
+def mean_embedding_per_property(
+    df: pd.DataFrame,
+    kb_labels: set[str] | None,
+    *,
+    embed_column: str = "embed",
+    property_column: str = "property",
+) -> dict[str, np.ndarray]:
+    """
+    Average all mention embeddings per property label (same logic as Linker single-file load).
+    """
+    if kb_labels is not None:
+        work = df[df[property_column].isin(kb_labels)].copy()
+    else:
+        work = df.copy()
+    if len(work) == 0:
+        return {}
+
+    work["_emb_arr"] = work[embed_column].apply(
+        lambda x: np.asarray(x, dtype=np.float64)
+    )
+    out: dict[str, np.ndarray] = {}
+    for prop_label, group in work.groupby(property_column):
+        stacked = np.stack(group["_emb_arr"].tolist())
+        out[str(prop_label)] = np.mean(stacked, axis=0)
+    return out
+
+
+def property_mean_vectors_per_parquet(
+    path: pathlib.Path,
+    kb_labels: set[str] | None,
+    *,
+    batch_size: int = 1000,
+    n_embedding_batches: int | None = None,
+) -> dict[str, np.ndarray]:
+    return property_mean_vectors_from_parquet_batches(
+        path,
+        kb_labels,
+        batch_size=batch_size,
+        n_embedding_batches=n_embedding_batches,
+    )
+
+
+def fused_property_vectors_from_paths(
+    paths: Sequence[pathlib.Path],
+    kb_labels: set[str] | None,
+    *,
+    batch_size: int = 1000,
+    n_embedding_batches: int | None = None,
+    read_status: Callable[[str], None] | None = None,
+    show_read_progress: bool = False,
+) -> dict[str, np.ndarray]:
+    """
+    Per-source per-property means, then concatenate vectors for properties in the intersection.
+
+    Source order matches ``paths`` order (must align with ``EmbeddingModelMetadata.sources``).
+    Parquet inputs are read in batches (same mechanism as clustering analysis on loaded frames).
+    """
+    if len(paths) == 0:
+        raise ValueError("paths must be non-empty")
+    n_paths = len(paths)
+    per_source: list[dict[str, np.ndarray]] = []
+    for pi, p in enumerate(paths):
+        prefix = f"[{pi + 1}/{n_paths}] " if n_paths > 1 else ""
+
+        def path_read(msg: str, _pfx: str = prefix) -> None:
+            if read_status is not None:
+                read_status(_pfx + msg)
+
+        per_source.append(
+            property_mean_vectors_from_parquet_batches(
+                p,
+                kb_labels,
+                batch_size=batch_size,
+                n_embedding_batches=n_embedding_batches,
+                read_status=path_read if read_status is not None else None,
+                show_read_progress=show_read_progress and read_status is None,
+            )
+        )
+    common = set(per_source[0].keys())
+    for d in per_source[1:]:
+        common &= set(d.keys())
+    if not common:
+        return {}
+
+    fused: dict[str, np.ndarray] = {}
+    for prop in sorted(common):
+        parts = [per_source[i][prop] for i in range(len(paths))]
+        fused[prop] = np.concatenate(parts, axis=0)
+    return fused
+
+
+def property_fused_dataframe_for_linker_order(
+    fused_vectors: Mapping[str, np.ndarray],
+    labels_map: Mapping[str, str],
+) -> pd.DataFrame:
+    """
+    Rows sorted like Linker fused load: sorted property labels that resolve to an
+    ``entity_id`` in ``labels_map``. Columns: ``entity_id``, ``property``, ``embed``.
+    """
+    rows: list[dict[str, object]] = []
+    for prop_label in sorted(fused_vectors.keys()):
+        entity_id = None
+        for eid, label in labels_map.items():
+            if label == prop_label:
+                entity_id = eid
+                break
+        if entity_id is None:
+            logger.warning(
+                "Property label '%s' not found in labels_map, skipping", prop_label
+            )
+            continue
+        vec = fused_vectors[prop_label]
+        rows.append(
+            {
+                "entity_id": entity_id,
+                "property": prop_label,
+                "embed": vec.astype(np.float32, copy=False),
+            }
+        )
+    if not rows:
+        return pd.DataFrame(columns=["entity_id", "property", "embed"])
+    return pd.DataFrame(rows)
diff --git a/pelinker/io/parquet.py b/pelinker/io/parquet.py
index 7e08415..491b652 100644
--- a/pelinker/io/parquet.py
+++ b/pelinker/io/parquet.py
@@ -72,7 +72,6 @@ def write_batch(self, data: List[dict]):
             if self.writer is None:
                 self.schema = self._get_schema()
                 self.writer = pq.ParquetWriter(self.output_path, self.schema)
-                logger.info(f"Initialized Parquet writer with schema: {self.schema}")
 
             if self.use_record_batch:
                 # Use RecordBatch for better memory efficiency
@@ -84,7 +83,6 @@ def write_batch(self, data: List[dict]):
                 self.writer.write_table(table)
 
             self.total_rows += len(data)
-            logger.info(f"Wrote batch with {len(data)} rows. Total: {self.total_rows}")
 
         except Exception as e:
             logger.error(f"Error writing batch: {e}")
diff --git a/pelinker/model.py b/pelinker/model.py
index cc9ad8e..73ea666 100644
--- a/pelinker/model.py
+++ b/pelinker/model.py
@@ -1,6 +1,10 @@
-import faiss
-from pelinker.util import texts_to_vrep
-from pelinker.onto import WordGrouping
+import sys
+import tempfile
+from collections import Counter
+from collections.abc import Sequence
+from dataclasses import replace
+
+import pandas as pd
 import torch
 import joblib
 import numpy as np
@@ -8,87 +12,133 @@
 import hdbscan
 from hdbscan import approximate_predict
 import pathlib
-import pyarrow.parquet as pq
 import logging
-from numpy.random import RandomState
 
-from pelinker.transform import EmbeddingTransformer, TransformConfig
+from pelinker.config import (
+    ClusteringOptimizationConfig,
+    EmbeddingModelMetadata,
+    EmbeddingTrainingConfig,
+    KBConfig,
+    TransformConfig,
+)
+from pelinker.analysis import (
+    drop_properties_with_few_mentions,
+    estimate_clustering_from_frame,
+    filter_mention_frame_by_kb_labels,
+    mention_frame_from_embedding_paths,
+)
+from pelinker.embedder import embed_kb_corpus
+from pelinker.embedding_fusion import (
+    fused_property_vectors_from_paths,
+    property_fused_dataframe_for_linker_order,
+)
+from pelinker.onto import WordGrouping
+from pelinker.transform import EmbeddingTransformer
+from pelinker.util import load_models, texts_to_vrep
 
 logger = logging.getLogger(__name__)
 
 
+def _modal_cluster_deterministic(clusters: list[int]) -> int | None:
+    """Most frequent cluster among ``clusters``, excluding HDBSCAN noise (-1); ties → smallest id."""
+    vals = [int(c) for c in clusters if int(c) != -1]
+    if not vals:
+        return None
+    cnt = Counter(vals)
+    best_n = max(cnt.values())
+    candidates = sorted(k for k, v in cnt.items() if v == best_n)
+    return candidates[0]
+
+
+def _provisional_cluster_assignments_from_training_frame(
+    labels_map: dict[str, str],
+    training: pd.DataFrame,
+) -> dict[str, int]:
+    """
+    Map each ``entity_id`` to a single cluster id for ``predict`` compatibility.
+
+    Heuristic: modal training cluster among rows whose ``property`` equals
+    ``labels_map[entity_id]``, ignoring -1. Interpretation of clusters is otherwise
+    left to downstream analysis (see ``Linker.training_cluster_frame``).
+    """
+    out: dict[str, int] = {}
+    if "property" not in training.columns or "cluster" not in training.columns:
+        return out
+    for entity_id, label in labels_map.items():
+        rows = training.loc[training["property"] == label, "cluster"]
+        if len(rows) == 0:
+            continue
+        mode = _modal_cluster_deterministic(rows.astype(int).tolist())
+        if mode is None:
+            continue
+        out[str(entity_id)] = int(mode)
+    return out
+
+
 class Linker:
     def __init__(
         self,
-        layers,
-        nb_nn=10,
-        index: faiss.IndexFlatIP | None = None,
-        transformer: Optional[EmbeddingTransformer] = None,
-        clusterer: Optional[hdbscan.HDBSCAN] = None,
-        cluster_assignments: Optional[dict[str, int]] = None,
-        transform_config: Optional[TransformConfig] = None,
+        transformer: EmbeddingTransformer | None = None,
+        clusterer: hdbscan.HDBSCAN | None = None,
+        transform_config: TransformConfig | None = None,
+        embedding_metadata: EmbeddingModelMetadata | None = None,
+        kb_config: KBConfig | None = None,
         **kwargs,
     ):
-        self.index: faiss.IndexFlatIP | None = index
-        # New clustering-based approach
-        self.transformer: Optional[EmbeddingTransformer] = transformer
-        self.clusterer: Optional[hdbscan.HDBSCAN] = clusterer
-        self.cluster_assignments: dict[str, int] = cluster_assignments or {}
-        self.transform_config: Optional[TransformConfig] = transform_config
+        self.transformer: EmbeddingTransformer | None = transformer
+        self.clusterer: hdbscan.HDBSCAN | None = clusterer
+        self.cluster_assignments: dict[str, int] = {}
+        self.transform_config: TransformConfig | None = transform_config
+        self.embedding_metadata: EmbeddingModelMetadata | None = embedding_metadata
+        self.kb_config: KBConfig | None = kb_config
 
         self.vocabulary: list[str] = []
         self.labels_map: dict[str, str] = kwargs.pop("labels_map", dict())
-        self.ls = layers
-        self.nb_nn = nb_nn
+        self.training_cluster_frame: pd.DataFrame | None = None
+        self._hf_tokenizer = None
+        self._hf_model = None
+        self._hf_models_by_type: dict[str, tuple[object, object]] = {}
 
     @classmethod
-    def filter_report(cls, report, thr_score, thr_dif):
-        report["entities"] = [
-            r
-            for r in report["entities"]
-            if r["dif_to_next"] >= thr_dif and r["score"] >= thr_score
-        ]
+    def filter_report(cls, report, thr_score):
+        report["entities"] = [r for r in report["entities"] if r["score"] >= thr_score]
         return report
 
     def dump(self, file_spec):
-        # Save FAISS index if it exists (legacy support)
-        if self.index is not None:
-            faiss.write_index(self.index, f"{file_spec}.index")
-            index_backup = self.index
-            self.index = None
-        else:
-            index_backup = None
-
         joblib.dump(self, f"{file_spec}.gz", compress=3)
 
-        # Restore index after saving
-        if index_backup is not None:
-            self.index = index_backup
-
     @classmethod
     def load(cls, file_spec):
         pe_model = joblib.load(f"{file_spec}.gz")
-        # Load FAISS index if it exists (legacy support)
-        index_path = f"{file_spec}.index"
-        try:
-            import pathlib
-
-            if pathlib.Path(index_path).exists():
-                index = faiss.read_index(index_path)
-                pe_model.index = index
-        except Exception:
-            # Index file doesn't exist or can't be loaded - that's OK for new models
-            pass
+        if "embedding_metadata" not in pe_model.__dict__:
+            pe_model.embedding_metadata = None
+        if "kb_config" not in pe_model.__dict__:
+            pe_model.kb_config = None
+        if "_hf_models_by_type" not in pe_model.__dict__:
+            pe_model._hf_models_by_type = {}
+        if "training_cluster_frame" not in pe_model.__dict__:
+            pe_model.training_cluster_frame = None
         return pe_model
 
+    @staticmethod
+    def _normalize_embedding_paths(
+        embeddings: pathlib.Path | Sequence[pathlib.Path],
+    ) -> list[pathlib.Path]:
+        if isinstance(embeddings, pathlib.Path):
+            return [embeddings.expanduser()]
+        return [pathlib.Path(p).expanduser() for p in embeddings]
+
     def fit(
         self,
-        embeddings: pathlib.Path,
+        embeddings: pathlib.Path | Sequence[pathlib.Path] | None,
         transform_config: TransformConfig,
         min_cluster_size: Optional[int] = None,
         kb_labels: Optional[set[str]] = None,
         optimize_clustering: bool = False,
-        clustering_optimization_params: Optional[dict] = None,
+        clustering_optimization_config: Optional[ClusteringOptimizationConfig] = None,
+        embedding_training: EmbeddingTrainingConfig | None = None,
+        embedding_metadata: EmbeddingModelMetadata | None = None,
+        kb_config: KBConfig | None = None,
     ):
         """
         Fit the Linker model with embeddings.
@@ -98,295 +148,411 @@ def fit(
         b) Clustering the embeddings
 
         Args:
-            embeddings: Either:
-                - Array of shape (n_samples, n_features) containing KB embeddings
-                - Path to parquet file containing embeddings (with 'property' and 'embed' columns)
+            embeddings: Path or sequence of paths to parquet file(s) (mention-level rows:
+                        ``pmid``, ``property``, ``mention``, ``embed``). Multiple files are
+                        fused like ``estimate_model_clustering`` (inner join on keys, concat
+                        embeddings). Order must match ``embedding_metadata.sources``.
+                        If None, ``embed_kb_corpus`` is run (one output file per source).
             transform_config: TransformConfig instance
-            min_cluster_size: Minimum cluster size for HDBSCAN. If None and optimize_clustering=False,
-                            uses default from transform_config or 20.
-            kb_labels: Set of KB property labels to filter by (only used when embeddings is a file path)
-            optimize_clustering: If True, optimize min_cluster_size using grid search
-            clustering_optimization_params: Optional dict with optimization parameters:
-                - min_class_size: Minimum class size for filtering (default: 20)
-                - max_scale: Maximum value for grid evaluation (default: 120)
-                - rns: RandomState for reproducibility (default: RandomState(seed=13))
-                - frac: Fraction of dataset to sample (default: 1.0)
-                - head: Number of batches to take (default: None)
-                - batch_size: Batch size for reading (default: 1000)
+            min_cluster_size: Minimum cluster size for HDBSCAN when ``optimize_clustering`` is
+                False. If None, uses 20.
+            kb_labels: Restrict training rows to these ``property`` labels (optional).
+            optimize_clustering: If True, run ``min_cluster_size`` grid search on a
+                ``clustering_optimization_config.frac`` subsample (analysis-aligned), then
+                fit the serialized model on **all** prepared rows (no subsampling).
+            clustering_optimization_config: Parquet batching (``batch_size``,
+                ``n_embedding_batches``), grid bounds (``min_class_size``, ``max_scale``),
+                grid subsample ``frac``, and RNG ``rns`` / ``frac`` for phase 1 only.
+            embedding_training: Corpus paths and embedding runtime. Required when embeddings=None.
+            embedding_metadata: If provided, overrides or sets ``self.embedding_metadata`` for
+                this fit (required when embeddings=None unless already set on the linker).
+            kb_config: Knowledge-base metadata stored on the linker; ``entity_count`` is set
+                from fitted vocabulary when omitted (None).
 
         Returns:
             self
         """
 
-        embeddings_path = pathlib.Path(embeddings)
-        # Part b) Optimize clustering first if requested (before loading embeddings)
+        is_temporary = False
+        embeddings_paths: list[pathlib.Path] = []
+
+        try:
+            if embedding_metadata is not None:
+                self.embedding_metadata = embedding_metadata
+
+            read_cfg = clustering_optimization_config or ClusteringOptimizationConfig()
+
+            if embeddings is None:
+                if embedding_training is None:
+                    raise ValueError(
+                        "embedding_training is required when embeddings is None. "
+                        "Provide embeddings path or EmbeddingTrainingConfig(...)."
+                    )
+                if self.embedding_metadata is None:
+                    raise ValueError(
+                        "embedding_metadata is required when embeddings is None "
+                        "(set on Linker(...) or pass embedding_metadata=... to fit())."
+                    )
+                k = len(self.embedding_metadata.sources)
+                for _ in range(k):
+                    tf = tempfile.NamedTemporaryFile(suffix=".parquet", delete=False)
+                    embeddings_paths.append(pathlib.Path(tf.name))
+                    tf.close()
+
+                logger.info("Stage (a): Embedding corpus (%s source(s))...", k)
+                embed_kb_corpus(
+                    metadata=self.embedding_metadata,
+                    training=embedding_training,
+                    output_parquet_paths=embeddings_paths,
+                )
+                is_temporary = True
+            else:
+                embeddings_paths = self._normalize_embedding_paths(embeddings)
+                if self.embedding_metadata is not None and len(embeddings_paths) != len(
+                    self.embedding_metadata.sources
+                ):
+                    raise ValueError(
+                        "Number of embedding parquet paths must match "
+                        f"embedding_metadata.sources ({len(self.embedding_metadata.sources)}), "
+                        f"got {len(embeddings_paths)}"
+                    )
+                logger.info(
+                    "Stage (A): Using provided embeddings (%s file(s)): %s",
+                    len(embeddings_paths),
+                    embeddings_paths,
+                )
+
+            logger.info(
+                "Stage (B): mention-level load from %s parquet file(s)",
+                len(embeddings_paths),
+            )
+            raw = mention_frame_from_embedding_paths(
+                embeddings_paths,
+                optimization_config=read_cfg,
+                show_read_progress=True,
+            )
+            if raw is None or len(raw) == 0:
+                raise ValueError(
+                    "No mention-level embedding rows loaded from parquet (check paths "
+                    "and columns pmid, property, mention, embed)."
+                )
+            filtered = filter_mention_frame_by_kb_labels(raw, kb_labels)
+            if len(filtered) == 0:
+                raise ValueError("No rows left after KB property-label filter")
+            prepared = drop_properties_with_few_mentions(
+                filtered, read_cfg.min_class_size
+            )
+            if len(prepared) == 0:
+                raise ValueError(
+                    "No rows left after dropping properties with fewer than "
+                    f"{read_cfg.min_class_size} mentions each"
+                )
+            prepared = prepared.reset_index(drop=True)
+
+            self._fit_clustering_on_prepared_mentions(
+                prepared=prepared,
+                transform_config=transform_config,
+                read_cfg=read_cfg,
+                optimize_clustering=optimize_clustering,
+                min_cluster_size_arg=min_cluster_size,
+                kb_config=kb_config,
+            )
+
+            return self
+        finally:
+            if is_temporary:
+                for p in embeddings_paths:
+                    try:
+                        p.unlink()
+                        logger.debug("Removed temporary parquet file: %s", p)
+                    except Exception as e:
+                        logger.warning(
+                            "Failed to remove temporary parquet file %s: %s", p, e
+                        )
+
+    def _fit_clustering_on_prepared_mentions(
+        self,
+        *,
+        prepared: pd.DataFrame,
+        transform_config: TransformConfig,
+        read_cfg: ClusteringOptimizationConfig,
+        optimize_clustering: bool,
+        min_cluster_size_arg: int | None,
+        kb_config: KBConfig | None,
+    ) -> None:
+        """Grid search (optional) on subsample; final PCA/UMAP + HDBSCAN on full ``prepared`` frame."""
         if optimize_clustering:
-            logger.info("Optimizing clustering parameters...")
-            min_cluster_size = self._optimize_clustering(
-                embeddings_path,
+            grid_sample = prepared.sample(
+                frac=read_cfg.frac,
+                random_state=read_cfg.rns,
+                replace=False,
+            )
+            if len(grid_sample) == 0:
+                logger.warning(
+                    "Empty grid subsample (frac=%s); using full prepared frame for grid",
+                    read_cfg.frac,
+                )
+                grid_sample = prepared
+            report = estimate_clustering_from_frame(
+                grid_sample,
                 transform_config,
-                kb_labels,
-                clustering_optimization_params,
+                optimization_config=read_cfg,
+                selected_labels=None,
+                all_metrics_dfs=None,
+                aggregation_level="mention",
+            )
+            if report is None:
+                logger.warning(
+                    "Clustering grid failed; falling back to min_class_size=%s",
+                    read_cfg.min_class_size,
+                )
+                best_mcs = int(read_cfg.min_class_size)
+            else:
+                best_mcs = int(report.hyperparameters.min_cluster_size)
+                logger.info(
+                    "Grid search selected min_cluster_size=%s (dbcv=%.4f)",
+                    best_mcs,
+                    report.best_score,
+                )
+        else:
+            best_mcs = int(
+                min_cluster_size_arg if min_cluster_size_arg is not None else 20
             )
 
-        # Load embeddings from file
-        logger.info(f"Loading embeddings from file: {embeddings_path}")
-        embeddings, entity_ids = self._load_embeddings_from_file(
-            embeddings_path, kb_labels
-        )
-        # Update vocabulary to match loaded entity_ids
-        self.vocabulary = entity_ids
-
-        # Set transform config
+        embeddings = np.stack(prepared["embed"].values).astype(np.float32, copy=False)
         self.transform_config = transform_config
-
-        # Fit transformer
-        self.transformer = EmbeddingTransformer(self.transform_config)
+        self.transformer = EmbeddingTransformer(transform_config)
         umap_clustering, _ = self.transformer.fit_transform(embeddings)
 
-        # Use optimized or provided min_cluster_size
-        if min_cluster_size is None:
-            min_cluster_size = 20  # Default
-
         self.clusterer = hdbscan.HDBSCAN(
-            min_cluster_size=min_cluster_size,
+            min_cluster_size=best_mcs,
             gen_min_span_tree=True,
-            prediction_data=True,  # Enable prediction for new points
+            prediction_data=True,
         )
-        cluster_labels = self.clusterer.fit_predict(umap_clustering)
+        cluster_labels_arr = self.clusterer.fit_predict(umap_clustering)
+        cluster_labels = cluster_labels_arr.astype(int, copy=False)
 
-        # Store cluster assignments: entity_id -> cluster_id
-        self.cluster_assignments = {
-            entity_id: int(cluster_id)
-            for entity_id, cluster_id in zip(self.vocabulary, cluster_labels)
-        }
-
-        return self
-
-    def _load_embeddings_from_file(
-        self, embeddings_path: pathlib.Path, kb_labels: Optional[set[str]] = None
-    ) -> tuple[np.ndarray, list[str]]:
-        """
-        Load embeddings from parquet file, filter to KB entities, and aggregate per property.
-
-        Args:
-            embeddings_path: Path to parquet file with 'property' and 'embed' columns
-            kb_labels: Optional set of KB property labels to filter by
-
-        Returns:
-            Tuple of (embeddings_array, entity_ids_list)
-        """
-        logger.info("Reading embedded corpus and filtering to KB entities...")
-        parquet_table = pq.read_table(embeddings_path)
-        df_parquet = parquet_table.to_pandas()
-
-        # Filter to only KB properties if labels provided
-        if kb_labels is not None:
-            df_kb_filtered = df_parquet[df_parquet["property"].isin(kb_labels)].copy()
-            logger.info(
-                f"Filtered to {len(df_kb_filtered)} mentions from {len(df_parquet)} total mentions"
+        tc_cols = ["pmid", "property", "mention"]
+        missing = [c for c in tc_cols if c not in prepared.columns]
+        if missing:
+            raise ValueError(
+                "Prepared mention frame missing columns required for "
+                f"training_cluster_frame: {missing}"
             )
-        else:
-            df_kb_filtered = df_parquet.copy()
-            logger.info(f"Using all {len(df_kb_filtered)} mentions (no KB filter)")
-
-        if len(df_kb_filtered) == 0:
-            raise ValueError("No mentions found for KB properties in corpus")
-
-        # Aggregate embeddings per property (average)
-        logger.info("Aggregating embeddings per property...")
-        # Convert embed lists to numpy arrays for aggregation
-        df_kb_filtered["embed_array"] = df_kb_filtered["embed"].apply(np.array)
-
-        # Group by property and average embeddings
-        property_embeddings = {}
-        for prop_label, group in df_kb_filtered.groupby("property"):
-            embeddings_list = group["embed_array"].tolist()
-            # Stack and average
-            embeddings_array = np.stack(embeddings_list)
-            avg_embedding = np.mean(embeddings_array, axis=0)
-            property_embeddings[prop_label] = avg_embedding
-
-        # Map property labels to entity_ids using labels_map
-        entity_ids = []
-        embeddings_list = []
-        for prop_label in sorted(property_embeddings.keys()):
-            # Find entity_id for this property label
-            entity_id = None
-            for eid, label in self.labels_map.items():
-                if label == prop_label:
-                    entity_id = eid
-                    break
-
-            if entity_id is not None:
-                entity_ids.append(entity_id)
-                embeddings_list.append(property_embeddings[prop_label])
-            else:
-                logger.warning(
-                    f"Property label '{prop_label}' not found in labels_map, skipping"
-                )
-
-        if len(embeddings_list) == 0:
-            raise ValueError("No valid embeddings after mapping to entity_ids")
-
-        embeddings = np.stack(embeddings_list)
+        self.training_cluster_frame = prepared[tc_cols].copy()
+        self.training_cluster_frame["cluster"] = cluster_labels
 
-        logger.info(
-            f"Embedded {len(embeddings)} KB properties into {embeddings.shape[1]}-dimensional vectors"
+        self.cluster_assignments = _provisional_cluster_assignments_from_training_frame(
+            self.labels_map,
+            self.training_cluster_frame,
         )
+        self.vocabulary = sorted(self.cluster_assignments.keys())
+        if not self.vocabulary:
+            raise ValueError(
+                "No entity_ids received provisional cluster assignments after fit "
+                "(check labels_map and training property labels)"
+            )
 
-        return embeddings, entity_ids
+        if kb_config is not None:
+            if kb_config.entity_count is None:
+                self.kb_config = replace(kb_config, entity_count=len(self.vocabulary))
+            else:
+                self.kb_config = kb_config
+
+    def _load_embeddings_from_file(
+        self, embeddings_path: pathlib.Path, kb_labels: set[str] | None = None
+    ) -> tuple[np.ndarray, list[str]]:
+        """Backward-compatible single-file loader; delegates to fused multi-file path."""
+        return self._load_fused_embeddings_from_files([embeddings_path], kb_labels)
 
-    def _optimize_clustering(
+    def _load_fused_embeddings_from_files(
         self,
-        embeddings_path: pathlib.Path,
-        transform_config: TransformConfig,
-        kb_labels: Optional[set[str]],
-        optimization_params: Optional[dict],
-    ) -> int:
+        embeddings_paths: Sequence[pathlib.Path],
+        kb_labels: set[str] | None = None,
+        *,
+        read_config: ClusteringOptimizationConfig | None = None,
+    ) -> tuple[np.ndarray, list[str]]:
         """
-        Optimize min_cluster_size using grid search.
+        Per-file per-property mean embeddings, intersection across sources, concat features.
 
-        Args:
-            embeddings_path: Path to parquet file
-            transform_config: TransformConfig instance
-            kb_labels: Set of KB property labels to filter by
-            optimization_params: Optional dict with optimization parameters
-
-        Returns:
-            Optimal min_cluster_size value
+        Legacy helper for property-level loads; ``Linker.fit`` uses mention-level fusion instead.
         """
-        from pelinker.analysis import estimate_model_clustering
-
-        # Default optimization parameters
-        params = {
-            "min_class_size": 20,
-            "max_scale": 120,
-            "rns": RandomState(seed=13),
-            "frac": 1.0,
-            "head": None,
-            "batch_size": 1000,
-            "optimization_method": "mean",
-        }
-        if optimization_params:
-            params.update(optimization_params)
-
-        logger.info("Fitting clustering model using estimate_model_clustering...")
-
-        clustering_report = estimate_model_clustering(
-            file_path=embeddings_path,
-            rns=params["rns"],
-            transform_config=transform_config,
-            min_class_size=params["min_class_size"],
-            max_scale=params["max_scale"],
-            frac=params["frac"],
-            head=params["head"],
-            batch_size=params["batch_size"],
-            selected_labels=kb_labels,
-            all_metrics_dfs=None,
-            optimization_method=params["optimization_method"],
+        cfg = read_config or ClusteringOptimizationConfig()
+        logger.info(
+            "Reading %s parquet source(s) and fusing per-property vectors...",
+            len(embeddings_paths),
         )
+        fused = fused_property_vectors_from_paths(
+            embeddings_paths,
+            kb_labels,
+            batch_size=cfg.batch_size,
+            n_embedding_batches=cfg.n_embedding_batches,
+            show_read_progress=sys.stdout.isatty(),
+        )
+        if not fused:
+            raise ValueError("No fused property vectors (empty intersection or inputs)")
 
-        if clustering_report is None:
-            logger.warning(
-                "Clustering estimation failed, using default min_cluster_size"
-            )
-            return params["min_class_size"]
+        dfr = property_fused_dataframe_for_linker_order(fused, self.labels_map)
+        if len(dfr) == 0:
+            raise ValueError("No valid embeddings after mapping to entity_ids")
 
-        best_min_cluster_size = clustering_report.best_size
-        best_score = clustering_report.best_score
+        embeddings = np.stack([np.asarray(e, dtype=np.float64) for e in dfr["embed"]])
+        entity_ids = list(dfr["entity_id"])
         logger.info(
-            f"Optimal min_cluster_size: {best_min_cluster_size} (score: {best_score:.3f})"
+            "Embedded %s KB properties into %s-dimensional fused vectors",
+            len(embeddings),
+            embeddings.shape[1],
         )
+        return embeddings, entity_ids
 
-        return best_min_cluster_size
+    def _ensure_hf_models_for_sources(self, *, use_gpu: bool = False) -> None:
+        """Load tokenizer+encoder once per distinct ``model_type`` in metadata sources."""
+        if self.embedding_metadata is None:
+            raise ValueError(
+                "embedding_metadata is required for predict(); set it during fit() or on the Linker."
+            )
+        for src in self.embedding_metadata.sources:
+            mt = src.model_type
+            if mt not in self._hf_models_by_type:
+                logger.info("Loading encoder for predict: %s", mt)
+                self._hf_models_by_type[mt] = load_models(mt, sentence=False)
+        if use_gpu and torch.cuda.is_available():
+            for _mt, (_tok, model) in self._hf_models_by_type.items():
+                model.to("cuda")
+        elif use_gpu:
+            logger.warning("CUDA is not available; predict runs on CPU")
+
+    @staticmethod
+    def _extract_ordered_mention_tensors(report_batch) -> list[torch.Tensor]:
+        word_groupings = [WordGrouping.W1, WordGrouping.W2, WordGrouping.W3]
+        tt_list: list[torch.Tensor] = []
+        for wg in word_groupings:
+            if wg not in report_batch.available_groupings():
+                continue
+            expression_container = report_batch[wg]
+            for expr_holder in expression_container.expression_data:
+                for _expr, tt in zip(expr_holder.expressions, expr_holder.tt):
+                    tt_list.append(tt)
+        return tt_list
+
+    @staticmethod
+    def _mention_tensor_lists_aligned(
+        lists: list[list[torch.Tensor]],
+    ) -> None:
+        n0 = len(lists[0])
+        for i, lst in enumerate(lists[1:], start=1):
+            if len(lst) != n0:
+                raise ValueError(
+                    f"Mention tensor count mismatch between fused sources: "
+                    f"source 0 has {n0}, source {i} has {len(lst)}. "
+                    "Use the same model_type for all sources if spans must align."
+                )
 
     def predict(
-        self, texts, tokenizer, model, nlp, max_length, topk=None, extra_context=False
+        self,
+        texts,
+        nlp,
+        max_length,
+        threshold: float = 0.0,
+        *,
+        use_gpu: bool = False,
     ):
         """
         Predict entities for input texts.
 
-        Uses clustering-based approach if transformer and clusterer are available,
-        otherwise falls back to FAISS index (legacy mode).
+        With multiple ``embedding_metadata.sources``, runs ``texts_to_vrep`` per source
+        (cached by ``model_type``), concatenates mention tensors along the feature axis in
+        source order, then applies the fitted transformer and clusterer. Mention counts
+        must match across sources (typically the same ``model_type`` for all sources).
         """
-        report_batch = texts_to_vrep(
-            texts,
-            tokenizer,
-            model,
-            layers_spec=self.ls,
-            word_modes=[WordGrouping.W1],
-            nlp=nlp,
-            max_length=max_length,
-        )
+        if self.embedding_metadata is None:
+            raise ValueError(
+                "embedding_metadata is required for predict(); set it during fit() or on the Linker."
+            )
+        self._ensure_hf_models_for_sources(use_gpu=use_gpu)
+
+        word_groupings = [WordGrouping.W1, WordGrouping.W2, WordGrouping.W3]
+        report_batches: list = []
+        for src in self.embedding_metadata.sources:
+            tok, model = self._hf_models_by_type[src.model_type]
+            rb = texts_to_vrep(
+                texts,
+                tok,
+                model,
+                layers_spec=src.layers_spec,
+                word_modes=word_groupings,
+                nlp=nlp,
+                max_length=max_length,
+            )
+            report_batches.append(rb)
 
-        # Extract embeddings and vocabulary from ReportBatch
-        wg_current = report_batch[WordGrouping.W1]
-
-        tt_list = []
-        vocabulary = []
-        for expr_holder in wg_current.expression_data:
-            # expr_holder is ExpressionHolder with tt (tensor) and expressions
-            for expr, tt in zip(expr_holder.expressions, expr_holder.tt):
-                tt_list.append(tt)
-                # Create item dict with mention text and position info
-                mention_text = ""
-                if expr.itext is not None and expr.itext < len(report_batch.texts):
-                    text = report_batch.texts[expr.itext]
-                    if expr.ichunk is not None:
-                        # Map chunk position to text position
-                        offset = report_batch.chunk_mapper.map_chunk_to_text(
-                            expr.itext, expr.ichunk
-                        )
-                        if expr.a is not None and expr.b is not None:
+        primary = report_batches[0]
+        tt_lists = [self._extract_ordered_mention_tensors(rb) for rb in report_batches]
+        self._mention_tensor_lists_aligned(tt_lists)
+
+        vocabulary: list[dict[str, object]] = []
+        for wg in word_groupings:
+            if wg not in primary.available_groupings():
+                continue
+            expression_container = primary[wg]
+            for expr_holder in expression_container.expression_data:
+                for expr, _tt in zip(expr_holder.expressions, expr_holder.tt):
+                    mention_text = ""
+                    if (
+                        expr.itext is not None
+                        and expr.itext < len(primary.texts)
+                        and expr.a is not None
+                        and expr.b is not None
+                    ):
+                        text = primary.texts[expr.itext]
+                        if expr.ichunk is not None:
+                            offset = primary.chunk_mapper.map_chunk_to_text(
+                                expr.itext, expr.ichunk
+                            )
                             mention_text = text[offset + expr.a : offset + expr.b]
-                    elif expr.a is not None and expr.b is not None:
-                        mention_text = text[expr.a : expr.b]
-
-                item_dict = {
-                    "mention": mention_text,
-                    "a": expr.a,
-                    "b": expr.b,
-                    "itext": expr.itext,
-                    "ichunk": expr.ichunk,
-                }
-                vocabulary.append(item_dict)
-
-        if not tt_list:
-            # No mentions found
-            report = {"entities": [], "word_groupings": {}}
-            return report
-
-        tt = torch.stack(tt_list)
-
-        # Use clustering-based approach if available
+                        else:
+                            mention_text = text[expr.a : expr.b]
+                    vocabulary.append(
+                        {
+                            "mention": mention_text,
+                            "a": expr.a,
+                            "b": expr.b,
+                            "itext": expr.itext,
+                            "ichunk": expr.ichunk,
+                            "word_grouping": wg,
+                        }
+                    )
+
+        if not tt_lists[0]:
+            return {"entities": [], "word_groupings": {}}
+
+        fused_rows: list[torch.Tensor] = []
+        for i in range(len(tt_lists[0])):
+            fused_rows.append(
+                torch.cat([tts[i] for tts in tt_lists], dim=-1),
+            )
+        tt = torch.stack(fused_rows, dim=0)
+
         if self.transformer is not None and self.clusterer is not None:
-            kb_items = self._predict_with_clustering(tt, vocabulary, topk=topk)
-        elif self.index is not None:
-            # Fall back to FAISS index (legacy mode)
-            distance_matrix, nearest_neighbors_matrix = self.index.search(
-                tt, self.nb_nn
+            kb_items = self._predict_with_clustering(
+                tt, vocabulary, threshold=threshold
             )
-            kb_items = []
-            for item, nn, d in zip(
-                vocabulary, nearest_neighbors_matrix, distance_matrix
-            ):
-                item = self.complement_with_kb_data(item, nn, d, topk=topk)
-                kb_items += [item]
         else:
             raise TypeError(
                 "Neither transformer/clusterer nor index is set. Call fit() first."
             )
 
-        # Convert to dict format for compatibility
-        report = {
+        return {
             "entities": kb_items,
-            "word_groupings": {WordGrouping.W1: wg_current},
+            "word_groupings": {
+                wg: primary[wg]
+                for wg in word_groupings
+                if wg in primary.available_groupings()
+            },
         }
-        return report
 
     def _predict_with_clustering(
-        self, embeddings: torch.Tensor, vocabulary: list, topk=None
+        self, embeddings: torch.Tensor, vocabulary: list, threshold: float = 0.0
     ):
         """
         Predict entities using clustering approach.
@@ -394,7 +560,8 @@ def _predict_with_clustering(
         Args:
             embeddings: Tensor of shape (n_mentions, embedding_dim)
             vocabulary: List of mention texts
-            topk: Number of top candidates to return
+            threshold: Minimum cluster membership probability required to return
+                a prediction
 
         Returns:
             List of entity predictions
@@ -419,6 +586,10 @@ def _predict_with_clustering(
         for item_dict, cluster_id, cluster_prob in zip(
             vocabulary, cluster_labels, cluster_probs
         ):
+            # Skip HDBSCAN outliers and low-confidence assignments.
+            if int(cluster_id) == -1 or float(cluster_prob) < threshold:
+                continue
+
             # Find all entities in the same cluster
             cluster_entities = [
                 entity_id
@@ -426,77 +597,21 @@ def _predict_with_clustering(
                 if cid == cluster_id
             ]
 
-            # If no entities in cluster (noise point), return empty
+            # Skip clusters that have no mapped entities from the training vocabulary.
             if not cluster_entities:
-                kb_item = {
-                    **item_dict,
-                    **{
-                        "entity_id_predicted": None,
-                        "score": float(cluster_prob),
-                        "dif_to_next": 0.0,
-                    },
-                }
-                kb_items += [kb_item]
                 continue
 
             # For now, return the first entity in the cluster
-            # TODO: Could implement similarity-based ranking within cluster
             predicted_entity = cluster_entities[0]
             score = float(cluster_prob)
 
-            # Calculate dif_to_next (difference to next cluster probability)
-            # For simplicity, use 0.0 if only one cluster
-            dif_to_next = 0.0
-            if len(cluster_entities) > 1:
-                # Could compute similarity to other entities in cluster
-                dif_to_next = 0.1  # Placeholder
-
             kb_item = {
                 **item_dict,
                 **{
                     "entity_id_predicted": predicted_entity,
                     "score": score,
-                    "dif_to_next": dif_to_next,
                 },
             }
-
-            if topk is not None and len(cluster_entities) > 1:
-                kb_item["_leading_candidates"] = cluster_entities[1 : topk + 1]
-                kb_item["_leading_scores"] = [score * 0.9] * min(
-                    len(cluster_entities) - 1, topk
-                )
             kb_items += [kb_item]
 
         return kb_items
-
-    def complement_with_kb_data(self, item, nearest_neighbors, distance, topk):
-        distance = distance.tolist()
-
-        candidate_entity = [self.vocabulary[nnx] for nnx in nearest_neighbors]
-
-        dif = round(distance[0] - distance[1], 5)
-        item = {
-            **item,
-            **{
-                "entity_id_predicted": candidate_entity[0],
-                "score": round(distance[0], 4),
-                "dif_to_next": dif,
-            },
-        }
-        if topk is not None:
-            item["_leading_candidates"] = candidate_entity[1:topk]
-            item["_leading_scores"] = [round(x, 4) for x in distance[1:topk]]
-
-        if self.labels_map:
-            item["entity_label"] = (
-                self.labels_map[candidate_entity[0]]
-                if candidate_entity[0] in self.labels_map
-                else "NA"
-            )
-            if topk is not None:
-                item["_leading_candidates_labels"] = [
-                    self.labels_map[e] if e in self.labels_map else "NA"
-                    for e in candidate_entity[1:topk]
-                ]
-
-        return item
diff --git a/pelinker/plotting.py b/pelinker/plotting.py
index 8ade7dc..b3853a6 100644
--- a/pelinker/plotting.py
+++ b/pelinker/plotting.py
@@ -1,15 +1,473 @@
 import pathlib
 
+import matplotlib
+import numpy as np
 import pandas as pd
 import seaborn as sns
-import matplotlib
 
 # Force a non-interactive backend because this project only saves plots to files.
 # This avoids Tk/threading crashes in long-running or parallelized workflows.
 matplotlib.use("Agg")
 from matplotlib import pyplot as plt
+from matplotlib.lines import Line2D
+from matplotlib.patches import Ellipse, Patch, Polygon, Rectangle, RegularPolygon, Wedge
 from plotly import express as px, graph_objects as go
 
+# Columns appended in ``run/analysis/clustering_quality.py`` grid export (per-sample summaries).
+GRID_COL_CHOSEN_MIN_CLUSTER_SIZE = "chosen_min_cluster_size"
+GRID_COL_SAMPLE_BEST_DBCV = "sample_best_dbcv"
+GRID_COL_SAMPLE_ARI = "sample_ari"
+
+# χ²(2) critical value at p≈0.95 for Gaussian 95% contour (no scipy).
+_CHI2_PPF_95_DF2 = 5.991464550106692
+
+# Arity → marker style: 1 = triangle, 2 = square (bimodal fill), 3 = circle (tricolor wedges).
+ARITY_MARKER_SCATTER: dict[str, str] = {
+    "singleton": "^",
+    "fusion2": "s",
+    "fusion3": "o",
+}
+
+_AXIS_MAX = 1.1
+_MARKER_HALF_WIDTH = 0.028
+_MIN_ELLIPSE_SEMI_AXIS = 0.018
+# Uniform footprint: square side = 2 * _MARKER_HALF_WIDTH; circle uses same radius; triangle side matches.
+_MARKER_FACE_ALPHA = 0.52
+_MARKER_OUTLINE_ALPHA = 0.72
+_ELLIPSE_FILL_ALPHA = 0.14
+_ELLIPSE_EDGE_ALPHA = 0.88
+_ELLIPSE_INFLATE = 1.12
+
+
+def _arity_from_model(model: str) -> str:
+    if model == "fusion2":
+        return "fusion2"
+    if model == "fusion3":
+        return "fusion3"
+    return "singleton"
+
+
+def _base_models_in_row(model: str, layer: str) -> list[str]:
+    """Constituent encoder model names (singleton: one; fusion: from ``a/L1+b/L2``)."""
+    if model not in ("fusion2", "fusion3"):
+        return [str(model)]
+    names: list[str] = []
+    for part in str(layer).split("+"):
+        p = part.strip()
+        if not p:
+            continue
+        m, _, _ = p.partition("/")
+        if m:
+            names.append(m)
+    return names if names else [str(model)]
+
+
+def _layer_spec_code(model: str, layer: str) -> str:
+    """
+    Short label for the layer configuration only (no encoder name): singleton ``layer`` as-is;
+    fusion ``a/L1+b/L2`` → ``L1+L2``.
+    """
+    if model not in ("fusion2", "fusion3"):
+        return str(layer)
+    specs: list[str] = []
+    for part in str(layer).split("+"):
+        p = part.strip()
+        if not p:
+            continue
+        _m, sep, lyr = p.partition("/")
+        specs.append(lyr if sep else p)
+    return ",".join(specs) if specs else str(layer)
+
+
+def _model_color_map(unique_models: list[str]) -> dict[str, str]:
+    models = sorted(set(unique_models))
+    palette = sns.color_palette("husl", n_colors=max(len(models), 1))
+    return {m: matplotlib.colors.to_hex(palette[i]) for i, m in enumerate(models)}
+
+
+def _rgba(hex_color: str, alpha: float) -> tuple[float, float, float, float]:
+    return matplotlib.colors.to_rgba(hex_color, alpha=alpha)
+
+
+def _merge_wedge_spans(
+    spans: list[tuple[float, float, str]],
+) -> list[tuple[float, float, str]]:
+    """Merge adjacent angular spans that share the same face color."""
+    if not spans:
+        return []
+    merged: list[tuple[float, float, str]] = []
+    t1, t2, c = spans[0]
+    for t1n, t2n, cn in spans[1:]:
+        if cn == c and t1n == t2:
+            t2 = t2n
+        else:
+            merged.append((t1, t2, c))
+            t1, t2, c = t1n, t2n, cn
+    merged.append((t1, t2, c))
+    return merged
+
+
+def _covariance_ellipse_95(
+    cov: np.ndarray,
+    *,
+    min_semi_axis: float = _MIN_ELLIPSE_SEMI_AXIS,
+) -> tuple[float, float, float] | None:
+    """
+    Return (width, height, angle_deg) for ~95% Gaussian ellipse.
+    Floors semi-axes so nearly-degenerate covariances stay visible.
+    """
+    c = np.asarray(cov, dtype=np.float64)
+    if c.shape != (2, 2):
+        return None
+    vals, vecs = np.linalg.eigh(c)
+    if np.any(vals < -1e-12):
+        return None
+    vals = np.maximum(vals, 0.0)
+    order = vals.argsort()[::-1]
+    vals, vecs = vals[order], vecs[:, order]
+    semi_major = max(float(np.sqrt(_CHI2_PPF_95_DF2 * vals[0])), min_semi_axis)
+    semi_minor = max(float(np.sqrt(_CHI2_PPF_95_DF2 * vals[1])), min_semi_axis)
+    width = 2.0 * semi_major
+    height = 2.0 * semi_minor
+    theta = float(np.degrees(np.arctan2(vecs[1, 0], vecs[0, 0])))
+    if not np.isfinite(width) or not np.isfinite(height):
+        return None
+    return width, height, theta
+
+
+def _draw_arity_marker(
+    ax: plt.Axes,
+    mx: float,
+    my: float,
+    *,
+    arity: str,
+    models: list[str],
+    color_by_model: dict[str, str],
+    zorder: int = 5,
+) -> None:
+    """Draw △ / □ / ○ in data coords; transparent fills; no internal seams for same-color adjacency."""
+    h = _MARKER_HALF_WIDTH
+    h_sq = 0.8 * h
+    fa = _MARKER_FACE_ALPHA
+    oa = _MARKER_OUTLINE_ALPHA
+    edge_rgba = (0.0, 0.0, 0.0, oa)
+
+    if arity == "singleton":
+        col = color_by_model.get(models[0], "#333333")
+        side = 2.0 * h
+        radius = float(side / np.sqrt(3.0))
+        tri = RegularPolygon(
+            (mx, my),
+            numVertices=3,
+            radius=radius,
+            orientation=np.pi / 2.0,
+            facecolor=_rgba(col, fa),
+            edgecolor=edge_rgba,
+            linewidth=0.85,
+            zorder=zorder,
+        )
+        ax.add_patch(tri)
+        return
+
+    if arity == "fusion2" and len(models) >= 2:
+        c0 = color_by_model.get(models[0], "#888888")
+        c1 = color_by_model.get(models[1], "#444444")
+        if c0 == c1:
+            sq = Polygon(
+                [
+                    (mx - h_sq, my - h_sq),
+                    (mx - h_sq, my + h_sq),
+                    (mx + h_sq, my + h_sq),
+                    (mx + h_sq, my - h_sq),
+                ],
+                closed=True,
+                facecolor=_rgba(c0, fa),
+                edgecolor=edge_rgba,
+                linewidth=0.85,
+                zorder=zorder,
+            )
+            ax.add_patch(sq)
+            return
+        left = Polygon(
+            [
+                (mx - h_sq, my - h_sq),
+                (mx - h_sq, my + h_sq),
+                (mx, my + h_sq),
+                (mx, my - h_sq),
+            ],
+            closed=True,
+            facecolor=_rgba(c0, fa),
+            linewidth=0,
+            zorder=zorder,
+        )
+        right = Polygon(
+            [
+                (mx, my - h_sq),
+                (mx, my + h_sq),
+                (mx + h_sq, my + h_sq),
+                (mx + h_sq, my - h_sq),
+            ],
+            closed=True,
+            facecolor=_rgba(c1, fa),
+            linewidth=0,
+            zorder=zorder,
+        )
+        ax.add_patch(left)
+        ax.add_patch(right)
+        ax.add_patch(
+            Rectangle(
+                (mx - h_sq, my - h_sq),
+                2 * h_sq,
+                2 * h_sq,
+                fill=False,
+                linewidth=0.9,
+                edgecolor=edge_rgba,
+                zorder=zorder + 1,
+            )
+        )
+        return
+
+    if arity == "fusion3" and len(models) >= 3:
+        r = h
+        base_spans = [(90.0, 210.0), (210.0, 330.0), (330.0, 450.0)]
+        colored = [
+            (t1, t2, color_by_model.get(models[i], "#666666"))
+            for i, (t1, t2) in enumerate(base_spans)
+        ]
+        for t1, t2, col in _merge_wedge_spans(colored):
+            w = Wedge(
+                (mx, my),
+                r,
+                t1,
+                t2,
+                facecolor=_rgba(col, fa),
+                linewidth=0,
+                zorder=zorder,
+            )
+            ax.add_patch(w)
+        ax.add_patch(
+            Ellipse(
+                (mx, my),
+                width=2 * r,
+                height=2 * r,
+                facecolor="none",
+                edgecolor=edge_rgba,
+                linewidth=0.9,
+                zorder=zorder + 1,
+            )
+        )
+        return
+
+    # Fallback: solid patch with arity-appropriate shape
+    mkey = models[0] if models else ""
+    col = color_by_model.get(mkey, "#333333")
+    if arity == "fusion2":
+        fb: Polygon | Ellipse = Polygon(
+            [
+                (mx - h, my - h),
+                (mx - h, my + h),
+                (mx + h, my + h),
+                (mx + h, my - h),
+            ],
+            closed=True,
+            facecolor=_rgba(col, fa),
+            edgecolor=edge_rgba,
+            linewidth=0.9,
+            zorder=zorder,
+        )
+    elif arity == "fusion3":
+        fb = Ellipse(
+            (mx, my),
+            width=2 * h,
+            height=2 * h,
+            facecolor=_rgba(col, fa),
+            edgecolor=edge_rgba,
+            linewidth=0.9,
+            zorder=zorder,
+        )
+    else:
+        side = 2.0 * h
+        radius = float(side / np.sqrt(3.0))
+        fb = RegularPolygon(
+            (mx, my),
+            numVertices=3,
+            radius=radius,
+            orientation=np.pi / 2.0,
+            facecolor=_rgba(col, fa),
+            edgecolor=edge_rgba,
+            linewidth=0.85,
+            zorder=zorder,
+        )
+    ax.add_patch(fb)
+
+
+def plot_dbcv_vs_ari_from_grid(
+    df_grid: pd.DataFrame,
+    output_path: pathlib.Path,
+) -> bool:
+    """
+    Scatter of mean DBCV vs mean ARI per (model, layer); shape = arity (△/□/○),
+    fill colors = base encoder model(s); text = layer spec only (e.g. fusion ``2+3``).
+    95% covariance ellipses when ``n_sample`` ≥ 2.
+
+    Expects ``sample_best_dbcv``, ``sample_ari`` on the grid export.
+    Both axes are fixed to ``[0, _AXIS_MAX]``.
+
+    Returns:
+        True if a figure was written, False if required data were absent.
+    """
+    needed = {
+        "model",
+        "layer",
+        "sample_idx",
+        GRID_COL_SAMPLE_BEST_DBCV,
+        GRID_COL_SAMPLE_ARI,
+    }
+    if not needed.issubset(df_grid.columns):
+        return False
+
+    df = df_grid.loc[
+        :,
+        [
+            "model",
+            "layer",
+            "sample_idx",
+            GRID_COL_SAMPLE_BEST_DBCV,
+            GRID_COL_SAMPLE_ARI,
+        ],
+    ].drop_duplicates(subset=["model", "layer", "sample_idx"], keep="first")
+    df = df[df[GRID_COL_SAMPLE_ARI].notna()].copy()
+    if df.empty:
+        return False
+
+    all_models: list[str] = []
+    for m, lyr in df[["model", "layer"]].drop_duplicates().itertuples(index=False):
+        all_models.extend(_base_models_in_row(str(m), str(lyr)))
+    color_by_model = _model_color_map(all_models)
+
+    fig, ax = plt.subplots(figsize=(8.5, 8.5))
+    arities_present: set[str] = set()
+
+    for (model, layer), g in df.groupby(["model", "layer"], sort=False):
+        xy = np.column_stack(
+            [
+                g[GRID_COL_SAMPLE_BEST_DBCV].to_numpy(dtype=np.float64),
+                g[GRID_COL_SAMPLE_ARI].to_numpy(dtype=np.float64),
+            ]
+        )
+        mean = xy.mean(axis=0)
+        n = xy.shape[0]
+        arity = _arity_from_model(str(model))
+        arities_present.add(arity)
+        models_row = _base_models_in_row(str(model), str(layer))
+
+        # Halo drawn first: light filled ellipse + clear dashed rim, slightly inflated so
+        # it remains visible through transparent markers.
+        if n >= 2:
+            cov = np.cov(xy, rowvar=False, ddof=1)
+            ell = _covariance_ellipse_95(cov)
+            if ell is not None:
+                w, h, ang = ell
+                wi, hi = w * _ELLIPSE_INFLATE, h * _ELLIPSE_INFLATE
+                patch = Ellipse(
+                    xy=(float(mean[0]), float(mean[1])),
+                    width=wi,
+                    height=hi,
+                    angle=ang,
+                    facecolor=(0.45, 0.48, 0.52, _ELLIPSE_FILL_ALPHA),
+                    edgecolor=(0.12, 0.14, 0.18, _ELLIPSE_EDGE_ALPHA),
+                    linewidth=1.45,
+                    linestyle=(0, (4.5, 3.0)),
+                    zorder=2,
+                )
+                ax.add_patch(patch)
+
+        _draw_arity_marker(
+            ax,
+            float(mean[0]),
+            float(mean[1]),
+            arity=arity,
+            models=models_row,
+            color_by_model=color_by_model,
+            zorder=5,
+        )
+        layer_code = _layer_spec_code(str(model), str(layer))
+        if len(layer_code) > 22:
+            layer_code = layer_code[:19] + "…"
+        ax.annotate(
+            layer_code,
+            (mean[0], mean[1]),
+            textcoords="offset points",
+            # xytext=(8, 8),
+            xytext=(-3 - 1.5 * (len(layer_code) - 1), -3),
+            fontsize=8,
+            alpha=0.88,
+            zorder=6,
+        )
+
+    ax.set_xlim(0.0, _AXIS_MAX)
+    ax.set_ylim(0.0, _AXIS_MAX)
+    ax.set_aspect("equal")
+    ax.set_xlabel("DBCV (per-sample best, mean over samples)")
+    ax.set_ylabel("Adjusted Rand Index (per-sample, mean over samples)")
+    ax.set_title("DBCV vs ARI; dashed ellipse ≈95% (n_sample >= 2)")
+
+    order_a = ["singleton", "fusion2", "fusion3"]
+    arity_labels = {
+        "singleton": "singleton",
+        "fusion2": "pair fusion",
+        "fusion3": "triple fusion",
+    }
+    edge_legend = (0.0, 0.0, 0.0, _MARKER_OUTLINE_ALPHA)
+    legend_shapes = [
+        Line2D(
+            [0],
+            [0],
+            marker=ARITY_MARKER_SCATTER[a],
+            color="none",
+            label=arity_labels[a],
+            markerfacecolor=_rgba("#bbbbbb", _MARKER_FACE_ALPHA),
+            markeredgecolor=edge_legend,
+            markersize=10,
+        )
+        for a in order_a
+        if a in arities_present
+    ]
+    legend_colors = [
+        Patch(
+            facecolor=_rgba(color_by_model[m], _MARKER_FACE_ALPHA),
+            edgecolor=edge_legend,
+            linewidth=0.5,
+            label=m,
+        )
+        for m in sorted(color_by_model.keys())
+    ]
+    if legend_shapes or legend_colors:
+        leg1 = ax.legend(
+            handles=legend_shapes,
+            title="Arity",
+            loc="upper left",
+            bbox_to_anchor=(1.02, 1.0),
+            borderaxespad=0.0,
+            frameon=True,
+        )
+        ax.add_artist(leg1)
+        ax.legend(
+            handles=legend_colors,
+            title="Base model",
+            loc="lower left",
+            bbox_to_anchor=(1.02, 0.0),
+            borderaxespad=0.0,
+            frameon=True,
+        )
+
+    ax.grid(True, alpha=0.28, linestyle="--", zorder=0)
+    plt.tight_layout()
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    plt.savefig(output_path, dpi=300, bbox_inches="tight")
+    plt.close(fig)
+    return True
+
 
 def plot_metrics_with_error_bars(
     metrics_list: list[pd.DataFrame],
@@ -307,4 +765,7 @@ def plot_metrics(df: pd.DataFrame, fname):
     plt.title("Clustering metrics vs. min_cluster_size (HDBSCAN)")
     fig.tight_layout()
 
-    plt.savefig(fname, bbox_inches="tight", dpi=300)
+    try:
+        plt.savefig(fname, bbox_inches="tight", dpi=300)
+    finally:
+        plt.close(fig)
diff --git a/pelinker/reporting.py b/pelinker/reporting.py
index 0fbecab..1cbb29e 100644
--- a/pelinker/reporting.py
+++ b/pelinker/reporting.py
@@ -1,19 +1,204 @@
+from __future__ import annotations
+
+from collections.abc import Sequence
 from dataclasses import dataclass
+import math
 
+import numpy as np
 import pandas as pd
 
 
+@dataclass(frozen=True)
+class MeanWithUncertainty:
+    """Sample mean and standard deviation (ddof=1) over repeated runs; ``std=0`` for a single run."""
+
+    mean: float
+    std: float
+
+
+@dataclass(frozen=True)
+class ClusteringHyperparameters:
+    """
+    HDBSCAN (and related) choices selected by the grid search / smoother.
+
+    Add fields here as more knobs participate in optimization; call sites then stay typed.
+    """
+
+    min_cluster_size: int
+
+
+@dataclass(frozen=True)
+class HyperparameterSearchStats:
+    """Distribution of chosen hyperparameters across repeated clustering samples."""
+
+    min_cluster_size: MeanWithUncertainty
+
+
 @dataclass
 class ClusteringReport:
-    """Report containing clustering analysis results."""
+    """Report containing clustering analysis results for one sample."""
 
-    best_size: int
+    hyperparameters: ClusteringHyperparameters
     best_score: float
+    """DBCV (``relative_validity_``) at the chosen ``min_cluster_size`` (mean when from aggregate)."""
+
     number_properties: int
-    metrics_df: (
-        pd.DataFrame
-    )  # DataFrame with columns: min_cluster_size, icm, n_clusters, dbcv
-    df: pd.DataFrame  # DataFrame with UMAP embeddings and cluster labels
-    hungarian_accuracy: float | None = (
-        None  # Hungarian matching accuracy (None if not computed)
+    """Count of distinct KB properties in the (filtered) frame used for clustering."""
+
+    n_clusters_emergent: int
+    """Number of HDBSCAN clusters at the chosen ``min_cluster_size`` (noise label -1 excluded)."""
+
+    metrics_df: pd.DataFrame
+    df: pd.DataFrame
+    ari: float | None = None
+
+    @property
+    def best_size(self) -> int:
+        """Backward-compatible alias for ``hyperparameters.min_cluster_size``."""
+        return self.hyperparameters.min_cluster_size
+
+
+@dataclass(frozen=True)
+class ClusteringSearchSummaryRow:
+    """
+    One row of the model×layer clustering search table (singleton or fusion label).
+
+    Use :meth:`to_flat_dict` for CSV / pandas / heatmaps (legacy column names).
+    """
+
+    model: str
+    layer: str
+    hyperparameters: HyperparameterSearchStats
+    number_properties: MeanWithUncertainty
+    n_clusters_emergent: MeanWithUncertainty
+    dbcv: MeanWithUncertainty
+    ari: MeanWithUncertainty | None
+
+    def to_flat_dict(self) -> dict[str, str | float | None]:
+        """Keys aligned with historical ``results.csv`` and ``plot_heatmap`` expectations."""
+        h = self.hyperparameters.min_cluster_size
+        p = self.number_properties
+        k = self.n_clusters_emergent
+        d = self.dbcv
+        row: dict[str, str | float | None] = {
+            "model": self.model,
+            "layer": self.layer,
+            "best_size": h.mean,
+            "best_size_std": h.std,
+            "number_properties": p.mean,
+            "number_properties_std": p.std,
+            "n_clusters_emergent": k.mean,
+            "n_clusters_emergent_std": k.std,
+            "best_score": d.mean,
+            "best_score_std": d.std,
+        }
+        if self.ari is None:
+            row["ari"] = None
+            row["ari_std"] = 0.0
+        else:
+            ari = self.ari
+            row["ari"] = ari.mean
+            row["ari_std"] = ari.std
+        return row
+
+
+def clustering_search_summary_row_from_flat_dict(
+    row: dict[str, str | float | None],
+) -> ClusteringSearchSummaryRow:
+    """Reconstruct :class:`ClusteringSearchSummaryRow` from :meth:`to_flat_dict` output."""
+    ari_raw = row.get("ari")
+    ari_block: MeanWithUncertainty | None
+    if ari_raw is None or (isinstance(ari_raw, float) and math.isnan(ari_raw)):
+        ari_block = None
+    else:
+        ari_block = MeanWithUncertainty(
+            mean=float(ari_raw),
+            std=float(row.get("ari_std") or 0.0),
+        )
+    return ClusteringSearchSummaryRow(
+        model=str(row["model"]),
+        layer=str(row["layer"]),
+        hyperparameters=HyperparameterSearchStats(
+            min_cluster_size=MeanWithUncertainty(
+                mean=float(row["best_size"]),
+                std=float(row["best_size_std"] or 0.0),
+            ),
+        ),
+        number_properties=MeanWithUncertainty(
+            mean=float(row["number_properties"]),
+            std=float(row["number_properties_std"] or 0.0),
+        ),
+        n_clusters_emergent=MeanWithUncertainty(
+            mean=float(row["n_clusters_emergent"]),
+            std=float(row["n_clusters_emergent_std"] or 0.0),
+        ),
+        dbcv=MeanWithUncertainty(
+            mean=float(row["best_score"]),
+            std=float(row["best_score_std"] or 0.0),
+        ),
+        ari=ari_block,
+    )
+
+
+def summarize_clustering_reports_for_search(
+    reports: Sequence[ClusteringReport],
+    *,
+    model: str,
+    layer: str,
+) -> ClusteringSearchSummaryRow:
+    """
+    Aggregate repeated :class:`ClusteringReport` runs into one search summary row.
+
+    Raises:
+        ValueError: if ``reports`` is empty.
+    """
+    if not reports:
+        raise ValueError("reports must be non-empty")
+
+    sizes = np.array(
+        [r.hyperparameters.min_cluster_size for r in reports], dtype=np.float64
+    )
+    scores = np.array([r.best_score for r in reports], dtype=np.float64)
+    nprops = np.array([r.number_properties for r in reports], dtype=np.float64)
+    n_clusters = np.array([r.n_clusters_emergent for r in reports], dtype=np.float64)
+    ari_vals = [float(r.ari) for r in reports if r.ari is not None]
+
+    n = len(reports)
+    std_sizes = float(np.std(sizes)) if n > 1 else 0.0
+    std_scores = float(np.std(scores)) if n > 1 else 0.0
+    std_nprops = float(np.std(nprops)) if n > 1 else 0.0
+    std_n_clusters = float(np.std(n_clusters)) if n > 1 else 0.0
+
+    ari_block: MeanWithUncertainty | None
+    if ari_vals:
+        arr = np.array(ari_vals, dtype=np.float64)
+        ari_block = MeanWithUncertainty(
+            mean=float(np.mean(arr)),
+            std=float(np.std(arr)) if len(arr) > 1 else 0.0,
+        )
+    else:
+        ari_block = None
+
+    return ClusteringSearchSummaryRow(
+        model=model,
+        layer=layer,
+        hyperparameters=HyperparameterSearchStats(
+            min_cluster_size=MeanWithUncertainty(
+                mean=float(np.mean(sizes)),
+                std=std_sizes,
+            ),
+        ),
+        number_properties=MeanWithUncertainty(
+            mean=float(np.mean(nprops)),
+            std=std_nprops,
+        ),
+        n_clusters_emergent=MeanWithUncertainty(
+            mean=float(np.mean(n_clusters)),
+            std=std_n_clusters,
+        ),
+        dbcv=MeanWithUncertainty(
+            mean=float(np.mean(scores)),
+            std=std_scores,
+        ),
+        ari=ari_block,
     )
diff --git a/pelinker/transform.py b/pelinker/transform.py
index 8cf7220..a246c14 100644
--- a/pelinker/transform.py
+++ b/pelinker/transform.py
@@ -7,7 +7,6 @@
 Pipeline: LLM embeddings -> PCA -> UMAP -> HDBSCAN
 """
 
-from dataclasses import dataclass
 from typing import Optional
 
 import numpy as np
@@ -15,35 +14,7 @@
 import umap
 from sklearn.decomposition import PCA
 
-
-@dataclass
-class TransformConfig:
-    """Configuration for the embedding transformation pipeline."""
-
-    # PCA configuration
-    pca_components: int = 50
-    """Number of principal components to keep after PCA reduction."""
-
-    # UMAP configuration
-    umap_components: int = 4
-    """Number of UMAP dimensions for clustering (typically 3-5)."""
-    umap_metric: str = "cosine"
-    """Distance metric for UMAP (default: 'cosine')."""
-
-    # Visualization UMAP configuration
-    umap_viz_components: int = 3
-    """Number of UMAP dimensions for visualization (default: 3)."""
-    umap_viz_metric: str = "cosine"
-    """Distance metric for visualization UMAP (default: 'cosine')."""
-
-    def __post_init__(self):
-        """Validate configuration parameters."""
-        if self.pca_components < 1:
-            raise ValueError("pca_components must be >= 1")
-        if self.umap_components < 2:
-            raise ValueError("umap_components must be >= 2")
-        if self.umap_viz_components < 2:
-            raise ValueError("umap_viz_components must be >= 2")
+from pelinker.config import TransformConfig
 
 
 class EmbeddingTransformer:
@@ -83,12 +54,19 @@ def fit(self, embeddings: np.ndarray) -> "EmbeddingTransformer":
 
         n_samples, n_features = embeddings.shape
 
-        # Fit PCA
-        self.pca = PCA(n_components=self.config.pca_components)
+        # PCA allows at most min(n_samples, n_features) components (svd_solver='full').
+        pca_n = min(self.config.pca_components, n_samples, n_features)
+        self.pca = PCA(n_components=pca_n)
         pca_reduced = self.pca.fit_transform(embeddings)
 
+        # UMAP requires n_neighbors < n_samples; cap the default (15) for tiny frames.
+        n_neighbors = min(15, max(2, n_samples - 1))
+        if n_neighbors >= n_samples:
+            n_neighbors = max(1, n_samples - 1)
+
         # Fit UMAP for clustering
         self.umap = umap.UMAP(
+            n_neighbors=n_neighbors,
             n_components=self.config.umap_components,
             metric=self.config.umap_metric,
         )
@@ -96,6 +74,7 @@ def fit(self, embeddings: np.ndarray) -> "EmbeddingTransformer":
 
         # Fit UMAP for visualization
         self.umap_viz = umap.UMAP(
+            n_neighbors=n_neighbors,
             n_components=self.config.umap_viz_components,
             metric=self.config.umap_viz_metric,
         )
@@ -153,7 +132,8 @@ def transform_embeddings(
     Transform embeddings in a DataFrame using PCA -> UMAP pipeline.
 
     This function adds columns to the DataFrame:
-    - PCA columns: p_00, p_01, ..., p_{pca_components-1:02d}
+    - PCA columns: p_00, p_01, ... (one per fitted component; may be fewer than
+      ``pca_components`` when the sample count is small)
     - UMAP clustering columns: u_00, u_01, ..., u_{umap_components-1:02d}
     - UMAP visualization columns: uviz_00, uviz_01, ..., uviz_{umap_viz_components-1:02d}
 
@@ -184,11 +164,12 @@ def transform_embeddings(
     umap_clustering = transformer.umap.transform(pca_reduced)
     umap_visualization = transformer.umap_viz.transform(pca_reduced)
 
-    # Create DataFrames for each transformation
+    # Create DataFrames for each transformation (widths follow fitted dims when PCA is capped).
+    n_pca = int(pca_reduced.shape[1])
     df_pca = pd.DataFrame(
         pca_reduced,
         index=df.index,
-        columns=[f"p_{j:02d}" for j in range(config.pca_components)],
+        columns=[f"p_{j:02d}" for j in range(n_pca)],
     )
 
     df_umap = pd.DataFrame(
diff --git a/pelinker/util.py b/pelinker/util.py
index 71fb592..0b7d478 100644
--- a/pelinker/util.py
+++ b/pelinker/util.py
@@ -1,14 +1,14 @@
 # pylint: disable=E1120
 
+import os
 import re
+from pathlib import Path
 
+from collections.abc import Callable
 from string import punctuation, whitespace
 from typing import List
-
-import tqdm
 from transformers import AutoModel, AutoTokenizer
 from sentence_transformers import SentenceTransformer
-import faiss
 import torch
 import pandas as pd
 from sklearn.metrics import accuracy_score
@@ -30,6 +30,13 @@
 logger = logging.getLogger(__name__)
 
 
+def expand_config_path(path: str | os.PathLike[str] | None) -> Path | None:
+    """Expand environment variables and ``~`` in config or CLI path strings."""
+    if path is None:
+        return None
+    return Path(os.path.expandvars(os.fspath(path))).expanduser()
+
+
 def load_models(model_type, sentence=False):
     if model_type == "scibert":
         spec = "allenai/scibert_scivocab_cased"
@@ -163,54 +170,6 @@ def map_spans_to_spans_basic(
     return map_ix_jx
 
 
-def aggregate_token_groups(nlp, text, extra_context=False):
-    doc = nlp(text)
-    context_tags = ["VB", "IN", "JJ", "TO"] if extra_context else ["VB"]
-
-    # tokens in reverse order
-    tokens = [token for token in doc if token.tag_[:2] in context_tags][::-1]
-
-    # group tokens
-    acc = [[]]
-    while tokens:
-        ctoken = tokens.pop()
-        group = acc[-1]
-        if group:
-            ixu, ixv = group[-1].i, ctoken.i
-            if ixv - ixu == 1:
-                group.append(ctoken)
-            else:
-                acc.append([ctoken])
-        else:
-            acc[0].append(ctoken)
-
-    # remove outstanding adjectives
-    def remove_bnd_adj(group):
-        if group and group[0].tag_ == "JJ":
-            group = group[1:]
-        if group and group[-1].tag_ == "JJ":
-            group = group[:-1]
-        return group
-
-    for jx, group in enumerate(acc):
-        group_new = remove_bnd_adj(group)
-        while len(group_new) != len(group):
-            group = group_new
-            group_new = remove_bnd_adj(group)
-        acc[jx] = group_new
-
-    token_groups = [
-        group for group in acc if any(item.tag_[:2] == "VB" for item in group)
-    ]
-    return token_groups
-
-
-def get_vb_spans(nlp, text, extra_context=False) -> list[tuple[int, int]]:
-    token_groups = aggregate_token_groups(nlp, text, extra_context)
-    spans = transform_tokens2spans(token_groups)
-    return spans
-
-
 def text_to_tokens(nlp, text) -> list[SimplifiedToken]:
     stokens = [
         SimplifiedToken(
@@ -228,11 +187,6 @@ def text_to_tokens(nlp, text) -> list[SimplifiedToken]:
     return stokens
 
 
-def transform_tokens2spans(token_groups) -> list[tuple[int, int]]:
-    spans = [(group[0].idx, group[-1].idx + len(group[-1])) for group in token_groups]
-    return spans
-
-
 def split_into_sentences(text):
     text = re.sub(r"\s+", " ", text)
 
@@ -371,37 +325,6 @@ def compute_distance_ref(
     return m0, dfa
 
 
-def embedding_to_dist(tt_x, tt_y):
-    index = faiss.IndexFlatIP(tt_x.shape[1])
-    nb_nn = min([100, tt_x.shape[0]])
-    index.add(tt_x)
-
-    distance_matrix, nearest_neighbors_matrix = index.search(tt_y, nb_nn)
-    ds = distance_matrix[:, 1:].flatten()
-    ds.sort()
-    k_links = 20
-    thr = ds[-k_links]
-
-    edges = []
-    for dd, nn in zip(distance_matrix, nearest_neighbors_matrix):
-        m = dd >= thr
-        equis = nn[m]
-        edges += [(equis[0], c) for c in equis[1:]]
-
-    dfa = pd.DataFrame(ds, columns=["dist"])
-    return dfa
-
-
-def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output[0]
-    input_mask_expanded = (
-        attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-    )
-    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
-        input_mask_expanded.sum(1), min=1e-9
-    )
-
-
 def encode(texts, tokenizer, model, ls):
     if ls == "sent":
         tt_labels = model.encode(texts, normalize_embeddings=True)
@@ -428,28 +351,6 @@ def fetch_latest_kb(path_derived) -> tuple[str | None, int]:
         return None, -1
 
 
-def split_long_text(text, max_length=MAX_LENGTH):
-    """
-
-    :param text:
-    :param max_length:
-    :return: list of strings representing text, such that each string is < max_length
-        text = " ".join(agg)
-    """
-    agg = []
-
-    for chunk in split_text_into_batches(text, max_length - 2):
-        if agg:
-            if len(agg[-1]) + len(chunk) < max_length - 2:
-                agg[-1] = agg[-1] + f" {chunk}"
-            else:
-                agg += [chunk]
-        else:
-            agg += [chunk]
-
-    return agg
-
-
 def split_text_into_batches(text: str, max_length) -> list[str]:
     pattern = (
         r"(.{1," + str(max_length - 1) + r"})(\s|$)|(.{1," + str(max_length) + r"})"
@@ -584,19 +485,6 @@ def texts_to_vrep(
     return ReportBatch(chunk_mapper=chunk_mapper, texts=texts, _data=data)
 
 
-def report2kb(report, wg):
-    wg_current = report["word_groupings"][wg]
-    tt_list = []
-    vocabulary = []
-    for sentence in wg_current:
-        tt_list += [t for _, t in sentence]
-        vocabulary += [item["mention"] for item, _ in sentence]
-    tt_basis = torch.concat(tt_list)
-    index = faiss.IndexFlatIP(tt_basis.shape[1])
-    index.add(tt_basis)
-    return vocabulary, index
-
-
 def merge_wbs(word_boundaries, window) -> list[tuple[int, int]]:
     return [
         (wa, wb)
@@ -698,10 +586,14 @@ def extract_and_embed_mentions(
     layers,
     batch_size,
     word_modes=(WordGrouping.W1, WordGrouping.W2, WordGrouping.W3),
+    on_encoder_batch: Callable[[int, int, int], None] | None = None,
 ) -> List[dict]:
     """
     Modified to return list of dicts instead of DataFrame for better memory management
     and consistent schema handling.
+
+    If ``on_encoder_batch`` is set, it is invoked after each encoder mini-batch with
+    ``(batch_index_0based, n_batches, n_mention_rows_accumulated)``.
     """
     data_pmids = pmids
 
@@ -714,7 +606,8 @@ def extract_and_embed_mentions(
     prop_tokens = {p: text_to_tokens(nlp=nlp, text=p) for p in entities}
 
     rows = []
-    for ibatch, text_batch in enumerate((pbar := tqdm.tqdm(data_batched))):
+    n_batches = len(data_batched)
+    for ibatch, text_batch in enumerate(data_batched):
         report_batch = texts_to_vrep(
             text_batch,
             tokenizer=tokenizer,
@@ -761,6 +654,7 @@ def extract_and_embed_mentions(
                         }
                     )
 
-        pbar.set_description(f"Entities added in chunk : {len(rows)}")
+        if on_encoder_batch is not None:
+            on_encoder_batch(ibatch, n_batches, len(rows))
 
     return rows
diff --git a/pyproject.toml b/pyproject.toml
index 1aa7a4b..519d426 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -2,33 +2,18 @@
 build-backend = "hatchling.build"
 requires = ["hatchling"]
 
-[dependency-groups]
-dev = [
-  "pre-commit>=4.0.1,<5",
-  "pytest>=9.0.2",
-  "seaborn>=0.13.2,<0.14"
-]
-gpu = [
-  "cupy-cuda12x>=13.6.0"
-]
-
 [project]
 authors = [{email = "abelikov@gmail.com", name = "Alexander Belikov"}]
 dependencies = [
   "cupy-cuda12x>=13.6.0",
   "dataclass-wizard>=0.35.1",
-  "faiss-cpu==1.8.0",
-  "flask-compress~=1.15",
-  "flask-cors>=6.0.2",
-  "flask-restful>=0.3.10,<0.4",
-  "flask>=3.0.3,<4",
+  "fastapi>=0.115.0,<1",
   "hdbscan>=0.8.41",
+  "hydra-core>=1.3.2",
   "joblib>=1.4.2,<2",
-  "numpy>=1.26.4,<2",
-  "pandas>=2.2.2,<3",
-  "plotly>=6.2.0,<7",
-  "pyarrow>=20.0.0,<21",
-  "rdflib>=7.0.0,<8",
+  "pandas>=2.3.3",
+  "pip>=26.0.1",
+  "pyarrow>=23.0.1",
   "rich>=14.1.0",
   "scikit-learn>=1.5.0,<2",
   "scikit-optimize>=0.10.2",
@@ -37,7 +22,7 @@ dependencies = [
   "torch>=2.9.1",
   "transformers>=4.41.2,<5",
   "umap-learn>=0.5.11",
-  "waitress>=3.0.2,<4",
+  "uvicorn>=0.42.0",
   "wordfreq>=3.1.1"
 ]
 description = "bio / sci property entity linker"
@@ -46,6 +31,17 @@ readme = "README.md"
 requires-python = ">=3.10.6"
 version = "0.1.3"
 
+[project.optional-dependencies]
+dev = [
+  "plotly>=6.2.0,<7",
+  "pre-commit>=4.0.1,<5",
+  "pytest>=9.0.2",
+  "seaborn>=0.13.2,<0.14"
+]
+gpu = [
+  "cupy-cuda12x>=13.6.0"
+]
+
 [project.scripts]
 pelinker-fit = "pelinker.cli.fit:run"
 pelinker-serves = "pelinker.cli.server:run"
diff --git a/run/README.md b/run/README.md
index 106cd3f..611944f 100644
--- a/run/README.md
+++ b/run/README.md
@@ -2,13 +2,16 @@
 
 This directory contains scripts for preprocessing knowledge bases, embedding corpora, and analyzing embedding quality.
 
+For a **single end-to-end train** (corpus embedding → KB filtering/aggregation → PCA/UMAP → optimized HDBSCAN clustering → serialized linker artifact), use the packaged CLI `pelinker.cli.fit` documented in [Fitting the linker model](#fitting-the-linker-model) below.
+
 ## Directory Structure
 
 ```
 run/
 ├── README.md                    # This file
 ├── embed_kb_corpus.py          # Embed knowledge base corpus
-├── loop.embed.kb.corpus.sh     # Batch embedding script
+├── loop.embed.kb.corpus.sh     # Batch embedding (grid over model × layer)
+├── loop.fit.sh                 # Batch full fit: same grid, runs pelinker-fit (A+B)
 ├── preprocessing/               # Property knowledge base generation
 │   ├── extract_properties_go.py    # Extract from GO-CAMs ontology
 │   ├── extract_properties_ro.py    # Extract from Relations Ontology
@@ -65,19 +68,113 @@ Merges properties from multiple sources (RO, GO, and custom properties) into a u
 
 ### `embed_kb_corpus.py`
 
-Embeds a knowledge base corpus using transformer models for downstream analysis and linking tasks.
+Embeds a knowledge base corpus using the same pipeline as **stage (A)** of `pelinker.cli.fit` (both call `pelinker.embedder.embed_kb_corpus`).
 
-- **Purpose**: Processes text corpora and extracts mentions of properties, generating embeddings for each mention
+- **Purpose**: Stream a text table, find KB property mentions, and write **mention-level** rows (with vectors) to Parquet.
 - **Inputs**:
-  - `--input-text-table-path`: TSV/CSV file with `pmid` and `text` columns (optionally gzipped)
-  - `--properties-txt-path`: Newline-separated list of properties/patterns to search for
-- **Output**: Parquet file containing extracted mentions with their embeddings
-- **Features**:
-  - Supports multiple model types (biobert, pubmedbert, scibert, etc.)
-  - Configurable layer selection for embeddings
-  - GPU acceleration support
-  - Streaming processing with chunking for large datasets
-  - Extracts mentions at multiple word grouping levels (W1, W2, W3)
+  - `--input-text-table-path`: TSV/CSV (optional gzip) with `pmid` and `text` columns; headers are auto-detected (same as fit).
+  - `--kb-csv-path`: Property KB CSV with **`label`** and **`entity_id`** (property labels are the patterns; same role as **`kb_path`** in `pelinker-fit`).
+  - `--output-parquet-path`: Destination Parquet (columns include **`property`**, **`embed`**, **`pmid`**, **`mention`**, etc.)—same artifact as **`embeddings_parquet`** in fit.
+- **Other useful flags**: `--model-type`, `--layers-spec`, `--use-gpu`, `--input-buffer-rows`, `--encoder-batch-size`, `--nlp-model`, `--max-input-buffers` (mirror `pelinker.cli.fit` defaults where applicable; default **`--model-type`** is `pubmedbert` to match fit). **`--encoder-batch-size`** is the transformer forward batch (GPU memory); **`--input-buffer-rows`** is only how many table rows pandas reads per pass.
+
+### `loop.embed.kb.corpus.sh` / `loop.fit.sh`
+
+- **`loop.embed.kb.corpus.sh`**: loops over the same default **`model_type` × `layers_spec`** grid and runs **`embed_kb_corpus.py`** only (Parquet per combo).
+- **`loop.fit.sh`**: same grid, but runs **`uv run pelinker-fit`** per combo—**stage (A)** writes `res_<model>_<layer_tag>.parquet` under **`--output-parquet-prefix`**, **stage (B)** writes **`pelinker.<model>.<layer_tag>.gz`** under **`--output-model-prefix`** (`layer_tag` is `layers_spec` with commas replaced by `_` for filenames). Requires four flags: `--input-text-table-path`, `--kb-csv-path`, `--output-parquet-prefix`, **`--output-model-prefix`**. Optional **`--layers`**: **`layers_spec`** list (default `1,2,3`). Comma separates distinct specs; use **semicolons** when one spec contains commas, e.g. `--layers 1,2,3`, `--layers 1`, or `--layers '1,2;3'` (runs `1,2` then `3`).
+
+## Fitting the linker model
+
+Module: **`pelinker.cli.fit`**. It runs the linker training pipeline in **two conceptual stages**:
+
+1. **Stage (A)** — `embed_kb_corpus(...)` (same function as `run/embed_kb_corpus.py`) when **`input_text_table_path`** is set: **`kb_path`** + text table → **`embeddings_parquet`**.
+2. **Stage (B)** — `Linker.fit(...)` on that Parquet: fusion / PCA / UMAP / optimized HDBSCAN → fitted linker; serialized via `Linker.dump` (joblib at **`{output_path}.gz`**; the `.gz` suffix is appended automatically).
+
+If you omit **`input_text_table_path`**, only **stage (B)** runs (you must already have **`embeddings_parquet`** on disk, e.g. from a prior `embed_kb_corpus.py` run).
+
+**How to run** (use `uv` so dependencies match `uv.lock`):
+
+- `uv run pelinker-fit …` (console script from `pyproject.toml`)
+- `uv run python -m pelinker.cli.fit …` (equivalent module invocation)
+
+Configuration uses [Hydra](https://hydra.cc/) **override** syntax: `key=value` arguments after the command. App defaults are composed from `FitCliConfig` in `pelinker/cli/fit.py` via `pelinker/conf/fit.yaml`.
+
+Hydra’s **`hydra.output_subdir`** defaults to **`null`** here (no `.hydra` folder under the run directory; Hydra still creates a timestamped `outputs/…` run dir unless you change `hydra.run.dir`). To use Hydra’s stock layout with a nested config snapshot directory, pass e.g. `hydra.output_subdir=.hydra`. The same default is set for **`pelinker-serves`** (`uv run python -m pelinker.cli.server`, `pelinker/conf/server.yaml`).
+
+### Parameter alignment with `embed_kb_corpus.py`
+
+| `pelinker-fit` (Hydra) | `run/embed_kb_corpus.py` (Click) |
+|------------------------|----------------------------------|
+| `kb_path` | `--kb-csv-path` |
+| `input_text_table_path` | `--input-text-table-path` |
+| `embeddings_parquet` | `--output-parquet-path` |
+| `model_type`, `layers_spec`, … | `--model-type`, `--layers-spec`, … |
+
+### Required inputs
+
+- **`kb_path`**: Property KB CSV. Must include **`label`** and **`entity_id`** (labels are corpus patterns; IDs map fused properties to linker vocabulary—the same schema as **`--kb-csv-path`** for embedding).
+- **`embeddings_parquet`**: Mention-level **Parquet** path—**output** of stage (A) and **input** of stage (B). For stage (B) only, it must already exist and match **`model_type`** / **`layers_spec`**.
+- **`input_text_table_path`** (optional): If set, stage (A) runs and **writes** **`embeddings_parquet`** via `embed_kb_corpus`. If omitted, stage (B) **reads** the existing file.
+
+### Optional parameters (defaults)
+
+| Override | Default | Meaning |
+|----------|---------|---------|
+| `model_type` | `pubmedbert` | Embedding backbone (same vocabulary as `embed_kb_corpus.py` / `EmbeddingModelMetadata`). |
+| `layers_spec` | `1` | Which layers to use (string parsed by `str2layers`; e.g. comma-separated indices). |
+| `pca_components` | `100` | PCA dimensionality before UMAP. |
+| `umap_dim` | `8` | UMAP output dimension for clustering. |
+| `min_class_size` | `20` | Lower bound for clustering optimization / HDBSCAN (see `ClusteringOptimizationConfig`). |
+| `max_scale` | `120` | Upper bound for the optimization grid over `min_cluster_size`. |
+| `output_path` | *(see below)* | Where `linker.dump` writes the artifact. |
+| `use_gpu` | `false` | GPU for transformer encoding when embedding the corpus. |
+| `input_buffer_rows` | `1000` | Stage (A): rows per pandas read pass over the text table (I/O buffer; does **not** control GPU memory). |
+| `encoder_batch_size` | `200` | Stage (A): table rows per encoder forward pass—**lower this if the GPU runs out of memory**. |
+| `frac` | `1.0` | Fraction of rows to keep when sampling the mention parquet for clustering optimization (stage B); same idea as `clustering_quality.py --frac`. |
+| `batch_size` | `1000` | Stage (B): rows per batch when **reading large embedding parquet files**; same role as `clustering_quality.py --batch-size`. |
+| `nlp_model` | `en_core_web_trf` | spaCy pipeline for mention extraction (`uv run spacy download en_core_web_trf`). |
+| `max_input_buffers` | *(unset)* | Stage (A): stop after this many text-table read passes (each up to `input_buffer_rows` rows); unrelated to `encoder_batch_size`. |
+| `n_embedding_batches` | *(unset)* | Stage B only: max parquet read batches per file (see `pelinker.analysis` / `clustering_quality.py --n-embedding-batches`). |
+| **`kb_name`** | stem of `kb_path` | Display name stored in `KBConfig`. |
+| **`kb_version`** | `0.1.0` | KB version string stored on the model. |
+| **`kb_created_at`** | today | ISO date string (`YYYY-MM-DD`); defaults to **today** if omitted. |
+| **`kb_description`** | `""` | Free-form KB description. |
+| **`kb_entity_count`** | *(unset)* | Optional; if omitted, may be filled from the fitted vocabulary in `KBConfig`. |
+
+**Default output location**: if `output_path` is not set, the model is written under the `pelinker.store` package resources as `pelinker.model.{model_type}.{layers_str}` (e.g. `pelinker.model.pubmedbert.1` → **`pelinker.model.pubmedbert.1.gz`** next to that package resource). Set `output_path` to an explicit filesystem path (without adding `.gz` yourself) for reproducible artifacts.
+
+### Examples
+
+Embed a corpus from the synthesized KB and save to a known path:
+
+```bash
+uv run pelinker-fit \
+  kb_path=data/derived/properties.synthesis.1.csv \
+  input_text_table_path=data/corpus/articles.tsv.gz \
+  embeddings_parquet=outputs/corpus_pubmedbert_1.parquet \
+  output_path=models/pelinker.pubmedbert.run1
+```
+
+Reuse parquet output from a prior `embed_kb_corpus.py` run and tune UMAP:
+
+```bash
+uv run python -m pelinker.cli.fit \
+  kb_path=data/derived/properties.synthesis.1.csv \
+  embeddings_parquet=outputs/res_pubmedbert_1.parquet \
+  umap_dim=12 \
+  output_path=models/pelinker.from_parquet
+```
+
+Short GPU smoke test truncating stage (A) after two table read passes (`input_buffer_rows` rows each unless the file ends sooner):
+
+```bash
+uv run pelinker-fit \
+  kb_path=data/derived/properties.synthesis.1.csv \
+  input_text_table_path=data/corpus/articles.tsv.gz \
+  embeddings_parquet=outputs/corpus_smoke_trunc.parquet \
+  max_input_buffers=2 \
+  input_buffer_rows=500 \
+  use_gpu=true
+```
 
 ## Analysis Scripts
 
@@ -92,7 +189,7 @@ Measures the quality of embeddings obtained from `embed_kb_corpus.py` by evaluat
 - **Outputs**:
   - `results.csv` - Summary table with metrics for each model/layer combination
   - `model.perf.heatmap.png` - Heatmap of best scores across models
-  - `model.hungarian_accuracy.heatmap.png` - Heatmap of Hungarian matching accuracy (if available)
+  - `model.ari.heatmap.png` - Heatmap of ARI clustering quality (if available)
   - `{model}_{layer}.png` - Metrics plots for each model/layer
   - `umap_best.html` - Interactive UMAP visualization of the best performing model
 - **Key Features**:
@@ -100,7 +197,7 @@ Measures the quality of embeddings obtained from `embed_kb_corpus.py` by evaluat
   - Optimizes cluster size using various metrics
   - Supports multiple sampling runs for statistical robustness
   - **Optional**: `--selected-labels-kb-path` parameter to evaluate quality over a specific subset of labels from a selected knowledge base CSV file
-- **Metrics**: Best cluster size, number of properties, clustering score, Hungarian matching accuracy
+- **Metrics**: Best cluster size, number of properties, clustering score, adjusted Rand index (ARI)
 
 ### `select_diverse_entities.py`
 
diff --git a/run/analysis/clustering_quality.py b/run/analysis/clustering_quality.py
index 63c40d7..5ce219b 100644
--- a/run/analysis/clustering_quality.py
+++ b/run/analysis/clustering_quality.py
@@ -1,32 +1,363 @@
 import click
+import gc
 import pathlib
-import numpy as np
+import sys
 
 import pandas as pd
 from numpy.random import RandomState
-from pelinker.plotting import (
-    plot_metrics_with_error_bars,
-    plot_heatmap,
-    plot_umap_viz,
-    plot_metrics,
-)
 from rich.console import Console
 from rich.progress import (
+    BarColumn,
     Progress,
     SpinnerColumn,
-    BarColumn,
     TextColumn,
     TimeElapsedColumn,
 )
 from rich.table import Table
 
-from pelinker.analysis import (
-    estimate_model_clustering,
+from pelinker.analysis import estimate_model_clustering
+from pelinker.clustering_fusion_ranking import (
+    singleton_items_by_dbcv_score,
+    top_k_fusion_candidates_by_dbcv_proxy,
+)
+from pelinker.clustering_quality_checkpoint import (
+    ClusteringQualityCheckpoint,
+    DEFAULT_CHECKPOINT_NAME,
+    FailureRecord,
+    RunMode,
+    combination_key_from_members,
+    compute_run_fingerprint,
+    fingerprint_config_from_cli,
+    load_checkpoint,
+    model_layer_from_singleton_key,
+    new_checkpoint,
+    reconcile_fusion_checkpoint_params,
+    save_checkpoint_atomic,
+    score_by_model_layer_from_checkpoint,
+    utc_now_iso,
 )
+from pelinker.config import ClusteringOptimizationConfig
 from pelinker.ops import parse_model_filename
+from pelinker.plotting import (
+    GRID_COL_CHOSEN_MIN_CLUSTER_SIZE,
+    GRID_COL_SAMPLE_BEST_DBCV,
+    GRID_COL_SAMPLE_ARI,
+    plot_dbcv_vs_ari_from_grid,
+    plot_heatmap,
+    plot_metrics,
+    plot_metrics_with_error_bars,
+    plot_umap_viz,
+)
+from pelinker.reporting import (
+    ClusteringReport,
+    ClusteringSearchSummaryRow,
+    clustering_search_summary_row_from_flat_dict,
+    summarize_clustering_reports_for_search,
+)
 from pelinker.transform import TransformConfig
 
 
+def _path_by_model_layer(
+    valid_files: list[tuple[pathlib.Path, str, str]],
+) -> dict[tuple[str, str], pathlib.Path]:
+    return {(m, layer): fp for fp, m, layer in valid_files}
+
+
+def _parse_fusion_members(layer_label: str) -> list[tuple[str, str]]:
+    members: list[tuple[str, str]] = []
+    for part in layer_label.split("+"):
+        p = part.strip()
+        model, _, layer = p.partition("/")
+        members.append((model, layer))
+    return sorted(members, key=lambda t: (t[0], t[1]))
+
+
+def _ordered_paths_for_fusion(
+    path_by_ml: dict[tuple[str, str], pathlib.Path],
+    members: list[tuple[str, str]],
+) -> list[pathlib.Path]:
+    return [path_by_ml[t] for t in members]
+
+
+def _update_leaderboard_fixed(
+    summary_row: ClusteringSearchSummaryRow,
+    *,
+    best_overall_score: float | None,
+    best_overall_model: str | None,
+    best_overall_layer: str | None,
+    best_per_model: dict[str, float],
+) -> tuple[float | None, str | None, str | None, dict[str, float]]:
+    mean_dbcv = summary_row.dbcv.mean
+    model, layer = summary_row.model, summary_row.layer
+    if not model.startswith("fusion"):
+        if best_overall_score is None or mean_dbcv > best_overall_score:
+            best_overall_score = mean_dbcv
+            best_overall_model = model
+            best_overall_layer = layer
+        if model not in best_per_model or mean_dbcv > best_per_model[model]:
+            best_per_model[model] = mean_dbcv
+    return best_overall_score, best_overall_model, best_overall_layer, best_per_model
+
+
+def _recompute_leaderboard_from_results(
+    results: list[ClusteringSearchSummaryRow],
+) -> tuple[float | None, str | None, str | None, dict[str, float]]:
+    best_overall_score = None
+    best_overall_model = None
+    best_overall_layer = None
+    best_per_model: dict[str, float] = {}
+    for r in results:
+        best_overall_score, best_overall_model, best_overall_layer, best_per_model = (
+            _update_leaderboard_fixed(
+                r,
+                best_overall_score=best_overall_score,
+                best_overall_model=best_overall_model,
+                best_overall_layer=best_overall_layer,
+                best_per_model=best_per_model,
+            )
+        )
+    return best_overall_score, best_overall_model, best_overall_layer, best_per_model
+
+
+def _materialize_best_report(
+    top: ClusteringSearchSummaryRow,
+    *,
+    valid_files: list[tuple[pathlib.Path, str, str]],
+    path_by_ml: dict[tuple[str, str], pathlib.Path],
+    transform_config: TransformConfig,
+    optimization_config: ClusteringOptimizationConfig,
+    selected_labels: set[str] | None,
+) -> ClusteringReport | None:
+    if top.model.startswith("fusion"):
+        members = _parse_fusion_members(top.layer)
+        try:
+            ordered_paths = _ordered_paths_for_fusion(path_by_ml, members)
+        except KeyError:
+            return None
+        return estimate_model_clustering(
+            transform_config=transform_config,
+            optimization_config=optimization_config,
+            file_paths=ordered_paths,
+            selected_labels=selected_labels,
+            all_metrics_dfs=None,
+            show_embedding_read_progress=sys.stdout.isatty(),
+        )
+    key = (top.model, top.layer)
+    path = path_by_ml.get(key)
+    if path is None:
+        return None
+    return estimate_model_clustering(
+        transform_config=transform_config,
+        optimization_config=optimization_config,
+        file_path=path,
+        selected_labels=selected_labels,
+        all_metrics_dfs=None,
+        show_embedding_read_progress=sys.stdout.isatty(),
+    )
+
+
+def _metrics_df_with_grid_sample_columns(
+    report: ClusteringReport,
+    *,
+    model: str,
+    layer: str,
+    sample_idx: int,
+) -> pd.DataFrame:
+    """Grid rows for ``results_grid_per_sample.csv`` with per-sample DBCV / ARI for scatter."""
+    ari = report.ari
+    return report.metrics_df.assign(
+        model=model,
+        layer=layer,
+        sample_idx=sample_idx,
+        **{
+            GRID_COL_CHOSEN_MIN_CLUSTER_SIZE: int(
+                report.hyperparameters.min_cluster_size
+            ),
+            GRID_COL_SAMPLE_BEST_DBCV: float(report.best_score),
+            GRID_COL_SAMPLE_ARI: float("nan") if ari is None else float(ari),
+        },
+    )
+
+
+def _fine_clustering_metadata_df(
+    report: ClusteringReport,
+    *,
+    model: str,
+    layer: str,
+    sample_idx: int,
+) -> pd.DataFrame:
+    """Per-sample clustering assignments for downstream analysis."""
+    cols = ["model", "layer", "sample_idx", "property", "class"]
+    optional_cols = ["pmid", "mention"]
+    present_optional = [c for c in optional_cols if c in report.df.columns]
+    keep = [
+        c for c in ["property", "class", *present_optional] if c in report.df.columns
+    ]
+    if "property" not in keep or "class" not in keep:
+        return pd.DataFrame(columns=cols + present_optional)
+    out = report.df[keep].copy()
+    out.insert(0, "sample_idx", sample_idx)
+    out.insert(0, "layer", layer)
+    out.insert(0, "model", model)
+    return out
+
+
+def _per_sample_grid_column_order() -> list[str]:
+    return [
+        "model",
+        "layer",
+        "sample_idx",
+        GRID_COL_CHOSEN_MIN_CLUSTER_SIZE,
+        GRID_COL_SAMPLE_BEST_DBCV,
+        GRID_COL_SAMPLE_ARI,
+        "min_cluster_size",
+        "icm",
+        "n_clusters",
+        "dbcv",
+    ]
+
+
+def _dedupe_per_sample_grid(df: pd.DataFrame) -> pd.DataFrame:
+    grid_cols = _per_sample_grid_column_order()
+    ordered = [c for c in grid_cols if c in df.columns]
+    tail = [c for c in df.columns if c not in ordered]
+    out = df[ordered + tail]
+    dup_subset = [c for c in grid_cols if c in out.columns]
+    if dup_subset:
+        out = out.drop_duplicates(subset=dup_subset, keep="last")
+    return out
+
+
+def _read_optional_csv(path: pathlib.Path) -> pd.DataFrame | None:
+    if not path.exists():
+        return None
+    try:
+        return pd.read_csv(path)
+    except Exception:
+        return None
+
+
+def _merge_new_frames_into_per_sample_grid_csv(
+    detail_path: pathlib.Path,
+    new_frames: list[pd.DataFrame],
+) -> None:
+    """Append grid rows to ``results_grid_per_sample.csv`` (merge + dedupe, atomic replace)."""
+    if not new_frames:
+        return
+    new_df = pd.concat(new_frames, ignore_index=True)
+    if new_df.empty:
+        return
+    prior = _read_optional_csv(detail_path)
+    if prior is not None and not prior.empty:
+        merged = pd.concat([prior, new_df], ignore_index=True)
+    else:
+        merged = new_df
+    merged = _dedupe_per_sample_grid(merged)
+    tmp = detail_path.with_suffix(detail_path.suffix + ".tmp")
+    merged.to_csv(tmp, index=False)
+    tmp.replace(detail_path)
+
+
+def _fine_metadata_dedupe_subset(df: pd.DataFrame) -> list[str]:
+    wanted = [
+        "model",
+        "layer",
+        "sample_idx",
+        "pmid",
+        "mention",
+        "property",
+        "class",
+    ]
+    return [c for c in wanted if c in df.columns]
+
+
+def _dedupe_fine_metadata_df(df: pd.DataFrame) -> pd.DataFrame:
+    cols = _fine_metadata_dedupe_subset(df)
+    if cols:
+        return df.drop_duplicates(subset=cols, keep="last")
+    return df
+
+
+def _merge_new_frames_into_fine_metadata_pickle(
+    fine_metadata_path: pathlib.Path,
+    new_frames: list[pd.DataFrame],
+) -> None:
+    if not new_frames:
+        return
+    new_df = pd.concat(new_frames, ignore_index=True)
+    if new_df.empty:
+        return
+    prior_frames: list[pd.DataFrame] = []
+    if fine_metadata_path.exists():
+        try:
+            prior = pd.read_pickle(fine_metadata_path, compression="gzip")
+            if prior is not None and not prior.empty:
+                prior_frames.append(prior)
+        except Exception:
+            pass
+    merged = pd.concat(prior_frames + [new_df], ignore_index=True)
+    merged = _dedupe_fine_metadata_df(merged)
+    tmp = fine_metadata_path.with_name(fine_metadata_path.name + ".tmp")
+    merged.to_pickle(tmp, compression="gzip")
+    tmp.replace(fine_metadata_path)
+
+
+def _mark_combination_done(
+    ckpt: ClusteringQualityCheckpoint,
+    ckpt_path: pathlib.Path,
+    *,
+    combination_key: str,
+    summary: ClusteringSearchSummaryRow,
+    singleton_score_key: str | None,
+) -> None:
+    if combination_key not in ckpt.completed_combinations:
+        ckpt.completed_combinations.append(combination_key)
+    flat = summary.to_flat_dict()
+    ckpt.summaries_by_key[combination_key] = dict(flat)
+    if singleton_score_key is not None:
+        ckpt.singleton_scores_by_key[singleton_score_key] = float(summary.dbcv.mean)
+    save_checkpoint_atomic(ckpt_path, ckpt)
+
+
+def _record_failure(
+    ckpt: ClusteringQualityCheckpoint,
+    ckpt_path: pathlib.Path,
+    *,
+    combination_key: str,
+    message: str,
+) -> None:
+    ckpt.failures.append(
+        FailureRecord(combination_key=combination_key, error=message, at=utc_now_iso())
+    )
+    save_checkpoint_atomic(ckpt_path, ckpt)
+
+
+def _singleton_score_by_model_layer_from_checkpoint(
+    ckpt: ClusteringQualityCheckpoint,
+) -> dict[tuple[str, str], float]:
+    """Mean DBCV per (model, layer) for fusion proxy (singletons only)."""
+    out = score_by_model_layer_from_checkpoint(ckpt.singleton_scores_by_key)
+    if out:
+        return out
+    for key, row in ckpt.summaries_by_key.items():
+        if not key.startswith("1:"):
+            continue
+        ml = model_layer_from_singleton_key(key)
+        score = row.get("best_score")
+        if score is not None:
+            out[ml] = float(score)
+    return out
+
+
+def _results_from_checkpoint(
+    ckpt: ClusteringQualityCheckpoint,
+) -> list[ClusteringSearchSummaryRow]:
+    return [
+        clustering_search_summary_row_from_flat_dict(dict(row))
+        for _k, row in sorted(ckpt.summaries_by_key.items(), key=lambda item: item[0])
+    ]
+
+
 @click.command()
 @click.option(
     "--input-dir",
@@ -71,16 +402,19 @@
     help="Fraction of dataset to sample",
 )
 @click.option(
-    "--head",
+    "--n-embedding-batches",
     type=click.INT,
     default=None,
-    help="Number of batches to take (None for all)",
+    help=(
+        "Max parquet read batches per file (see --batch-size for rows per batch); "
+        "omit to read all batches"
+    ),
 )
 @click.option(
     "--batch-size",
     type=click.INT,
     default=1000,
-    help="Batch size for reading files",
+    help="Rows per batch when reading mention-level embedding parquet files",
 )
 @click.option(
     "--prefix",
@@ -103,10 +437,53 @@
 @click.option(
     "--max-scale",
     type=click.INT,
-    default=120,
+    default=60,
     show_default=True,
     help="Maximum value for grid evaluation of min_cluster_size",
 )
+@click.option(
+    "--fusion-pairs",
+    type=click.INT,
+    default=5,
+    show_default=True,
+    help=(
+        "After scoring single embeddings (DBCV), evaluate fused pairs: "
+        "pick this many distinct pairs with highest sum of singleton DBCV. 0 disables."
+    ),
+)
+@click.option(
+    "--fusion-triples",
+    type=click.INT,
+    default=0,
+    show_default=True,
+    help=("Same as --fusion-pairs but for three-way fusions (costly). 0 disables."),
+)
+@click.option(
+    "--resume/--no-resume",
+    default=True,
+    show_default=True,
+    help=(
+        "If the checkpoint file exists and matches the run fingerprint, skip completed work. "
+        "If the file is missing, start fresh and create it. Use --no-resume to ignore an "
+        "existing checkpoint and reinitialize (overwrites on save)."
+    ),
+)
+@click.option(
+    "--checkpoint-path",
+    type=click.Path(path_type=pathlib.Path),
+    default=None,
+    help=f"Checkpoint JSON path (default: <output-dir>/{DEFAULT_CHECKPOINT_NAME})",
+)
+@click.option(
+    "--mode",
+    type=click.Choice(["single", "fusion2", "fusion3", "all"]),
+    default="all",
+    show_default=True,
+    help=(
+        "single: only single-embedding combinations; fusion2/fusion3: only that fusion order "
+        "(requires prior singleton scores in checkpoint); all: singletons then enabled fusions."
+    ),
+)
 def main(
     input_dir: pathlib.Path,
     output_dir: pathlib.Path,
@@ -115,24 +492,34 @@ def main(
     min_class_size: int,
     seed: int,
     frac: float,
-    head: int,
+    n_embedding_batches: int | None,
     batch_size: int,
     n_sample: int,
     prefix: str,
     selected_labels_kb_path: pathlib.Path | None,
     max_scale: int,
+    fusion_pairs: int,
+    fusion_triples: int,
+    resume: bool,
+    checkpoint_path: pathlib.Path | None,
+    mode: RunMode,
 ):
     """
     Process multiple parquet files and compute optimal cluster sizes.
 
     Files should follow the pattern: <prefix>_<model>_<layer>.parquet
+
+    After scoring each (model, layer) alone (mean DBCV as ``best_score``), optionally
+    evaluates fused embeddings: pairs/triples with the highest sum of singleton DBCV
+    scores (see ``--fusion-pairs`` / ``--fusion-triples``), then clusters the
+    concatenated mention-level vectors via ``estimate_model_clustering(..., file_paths=...)``.
+
+    Checkpointing is on by default (``--resume``): progress is saved under the output directory.
+    Use ``--no-resume`` to discard the on-disk checkpoint and start from an empty state.
     """
-    # Configure console to work better in PyCharm and other IDEs
-    # Use legacy_windows=False and force_terminal to ensure progress bars work
     console = Console(force_terminal=True, width=120, legacy_windows=False)
     input_dir = input_dir.expanduser()
 
-    # Load selected labels KB if provided
     selected_labels: set[str] | None = None
     if selected_labels_kb_path is not None:
         selected_labels_kb_path = selected_labels_kb_path.expanduser()
@@ -147,8 +534,6 @@ def main(
         )
         try:
             df_selected = pd.read_csv(selected_labels_kb_path)
-            # Extract labels from the selected labels KB
-            # The file should have a 'label' column
             if "label" not in df_selected.columns:
                 console.print(
                     f"[red]Selected labels KB file must have a 'label' column. Found columns: {list(df_selected.columns)}[/red]"
@@ -162,231 +547,564 @@ def main(
             console.print(f"[red]Error loading selected labels KB: {e}[/red]")
             return
 
-    # Set up output directory
     if output_dir is None:
         output_dir = input_dir
     else:
         output_dir = output_dir.expanduser()
     output_dir.mkdir(parents=True, exist_ok=True)
+    output_path = output_dir / "results.csv"
+    detail_path = output_dir / "results_grid_per_sample.csv"
+    fine_metadata_path = output_dir / "fine_clustering_metadata.pkl.gz"
+    if not resume:
+        for artifact in (detail_path, fine_metadata_path):
+            try:
+                if artifact.exists():
+                    artifact.unlink()
+            except OSError:
+                pass
+
+    fp_payload = fingerprint_config_from_cli(
+        input_dir=input_dir,
+        umap_dim=umap_dim,
+        pca_components=pca_components,
+        min_class_size=min_class_size,
+        seed=seed,
+        frac=frac,
+        n_embedding_batches=n_embedding_batches,
+        batch_size=batch_size,
+        prefix=prefix,
+        n_sample=n_sample,
+        selected_labels_kb_path=selected_labels_kb_path,
+        max_scale=max_scale,
+    )
+    run_fingerprint = compute_run_fingerprint(fp_payload)
+
+    ckpt_path = (
+        checkpoint_path.expanduser()
+        if checkpoint_path is not None
+        else output_dir / DEFAULT_CHECKPOINT_NAME
+    )
+
+    if resume and ckpt_path.exists():
+        ckpt = load_checkpoint(ckpt_path)
+        if ckpt.run_fingerprint != run_fingerprint:
+            console.print(
+                "[red]Checkpoint run fingerprint does not match current CLI parameters.[/red]\n"
+                f"Checkpoint: {ckpt.run_fingerprint}\n"
+                f"Current:    {run_fingerprint}\n"
+                "Use the same inputs, or pass --no-resume to reinitialize the checkpoint."
+            )
+            return
+        console.print(
+            f"[green]Resuming from checkpoint[/green] [cyan]{ckpt_path}[/cyan]"
+        )
+    else:
+        ckpt = new_checkpoint(run_fingerprint)
+        if resume:
+            console.print(
+                f"[cyan]No checkpoint at[/cyan] [yellow]{ckpt_path}[/yellow][cyan]; "
+                f"starting new run (writing checkpoint to[/cyan] [green]{ckpt_path}[/green][cyan]).[/cyan]"
+            )
+        else:
+            console.print(
+                f"[cyan]New run (--no-resume); checkpoint reinitialized at[/cyan] "
+                f"[green]{ckpt_path}[/green]"
+            )
+
+    n_fusion_cleared = reconcile_fusion_checkpoint_params(
+        ckpt,
+        fusion_pairs=fusion_pairs,
+        fusion_triples=fusion_triples,
+    )
+    if n_fusion_cleared > 0:
+        console.print(
+            f"[yellow]Fusion settings changed relative to the checkpoint; "
+            f"dropped {n_fusion_cleared} cached fusion row(s). Singletons are unchanged.[/yellow]"
+        )
+    save_checkpoint_atomic(ckpt_path, ckpt)
+
+    completed = set(ckpt.completed_combinations)
+    results: list[ClusteringSearchSummaryRow] = _results_from_checkpoint(ckpt)
+    (
+        best_overall_score,
+        best_overall_model,
+        best_overall_layer,
+        best_per_model,
+    ) = _recompute_leaderboard_from_results(results)
 
-    # Create transform config from CLI options
     transform_config = TransformConfig(
         pca_components=pca_components,
         umap_components=umap_dim,
     )
 
-    # Find all parquet files matching the pattern
     parquet_files = sorted(input_dir.glob(f"{prefix}*.parquet"))
-
     if not parquet_files:
         console.print(
             f"[red]No parquet files found matching pattern '{prefix}*.parquet' in {input_dir}[/red]"
         )
         return
 
-    # Filter valid files first
-    valid_files = []
+    valid_files: list[tuple[pathlib.Path, str, str]] = []
     for file_path in parquet_files:
         if not file_path.exists():
             continue
-
         model, layer = parse_model_filename(file_path.name, prefix)
         if model is None or layer is None:
             continue
-
-        valid_files.append((file_path, model, layer))
+        # Layer as str matches checkpoint keys from "1:model/layer" and score_by_ml lookups.
+        valid_files.append((file_path, model, str(layer)))
 
     if not valid_files:
         console.print("[red]No valid files to process[/red]")
         return
 
-    # Process each file with progress bar
-    results = []
-    best_overall_score = None
-    best_overall_model = None
-    best_overall_layer = None
-    best_per_model = {}  # Track best score per model
-    metrics_by_file = {}  # Store metrics for each (model, layer) combination
-    best_report = None  # Track the absolute best report for UMAP visualization
-
-    total_tasks = len(valid_files) * n_sample
-    with Progress(
-        SpinnerColumn(),
-        TextColumn("[bold blue]{task.description}"),
-        BarColumn(),
-        TextColumn("[progress.percentage]{task.percentage:>3.0f}%"),
-        TimeElapsedColumn(),
-        console=console,
-        refresh_per_second=4,  # Refresh rate for better PyCharm compatibility
-    ) as progress:
-        task = progress.add_task(
-            f"[cyan]Processing {len(valid_files)} files × {n_sample} samples...",
-            total=total_tasks,
+    path_by_ml = _path_by_model_layer(valid_files)
+    metrics_by_file: dict[tuple[str, str], list[pd.DataFrame]] = {}
+    best_report: ClusteringReport | None = None
+    detailed_grid_frames: list[pd.DataFrame] = []
+    fine_metadata_frames: list[pd.DataFrame] = []
+
+    run_single = mode in ("single", "all")
+    run_fusion2 = mode in ("fusion2", "all")
+    run_fusion3 = mode in ("fusion3", "all")
+
+    if mode in ("fusion2", "fusion3"):
+        if not _singleton_score_by_model_layer_from_checkpoint(ckpt):
+            console.print(
+                "[red]Fusion mode requires singleton scores in the checkpoint.[/red] "
+                "Run with ``--mode single`` (or ``all``) first, "
+                "or ensure summaries for ``1:...`` combinations exist."
+            )
+            return
+
+    if mode in ("fusion2", "fusion3"):
+        expected_singletons = {
+            combination_key_from_members([(m, layer)]) for _fp, m, layer in valid_files
+        }
+        missing = [k for k in sorted(expected_singletons) if k not in completed]
+        if missing:
+            console.print(
+                f"[yellow]Warning:[/yellow] {len(missing)} singleton combination(s) "
+                "are not marked complete in the checkpoint; fusion proxy scores may be incomplete."
+            )
+
+    # --- single-embedding combinations (arity 1) ---
+    if run_single:
+        ckpt.stages["single"] = "in_progress"
+        save_checkpoint_atomic(ckpt_path, ckpt)
+
+        total_tasks = len(valid_files) * n_sample
+        done_tasks = sum(
+            n_sample
+            for _fp, m, layer in valid_files
+            if combination_key_from_members([(m, layer)]) in completed
         )
+        initial_total = total_tasks
 
-        for file_path, model, layer in valid_files:
-            # Accumulate metrics across runs for this file
-            file_metrics = []
-            file_reports = []
-            all_metrics_dfs = []  # Accumulate metrics across samples for aggregation
-
-            rns = RandomState(seed=seed)
-
-            for sample_idx in range(n_sample):
-                # Update progress bar description with current status
-                status_parts = [
-                    f"[cyan]{model}[/cyan]/[yellow]{layer}[/yellow]",
-                    f"sample {sample_idx + 1}/{n_sample}",
-                ]
-
-                if best_overall_score is not None:
-                    status_parts.append(
-                        f"[green]Best: {best_overall_score:.3f}[/green] "
-                        f"([cyan]{best_overall_model}[/cyan]/[yellow]{best_overall_layer}[/yellow])"
-                    )
+        with Progress(
+            SpinnerColumn(),
+            TextColumn("[bold blue]{task.description}"),
+            BarColumn(),
+            TextColumn("[progress.percentage]{task.percentage:>3.0f}%"),
+            TimeElapsedColumn(),
+            console=console,
+            refresh_per_second=4,
+        ) as progress:
+            task = progress.add_task(
+                f"[cyan]Singletons: {len(valid_files)} files × {n_sample} samples...",
+                total=initial_total,
+                completed=done_tasks,
+            )
 
-                if model in best_per_model:
-                    status_parts.append(
-                        f"[magenta]{model}: {best_per_model[model]:.3f}[/magenta]"
-                    )
+            for file_path, model, layer in valid_files:
+                comb_key = combination_key_from_members([(model, layer)])
+                if comb_key in completed:
+                    progress.advance(task, advance=n_sample)
+                    continue
 
-                progress.update(task, description=" | ".join(status_parts))
+                file_metrics: list[pd.DataFrame] = []
+                file_reports: list[ClusteringReport] = []
+                all_metrics_dfs: list[pd.DataFrame] = []
 
-                report = estimate_model_clustering(
-                    file_path=file_path,
-                    rns=rns,
-                    transform_config=transform_config,
+                optimization_config = ClusteringOptimizationConfig(
                     min_class_size=min_class_size,
                     max_scale=max_scale,
+                    rns=RandomState(seed=seed),
                     frac=frac,
-                    head=head,
+                    n_embedding_batches=n_embedding_batches,
                     batch_size=batch_size,
-                    selected_labels=selected_labels,
-                    all_metrics_dfs=all_metrics_dfs,
                     optimization_method="mean",
                 )
 
-                if report is not None:
-                    # Collect metrics for plotting (function already updated all_metrics_dfs)
-                    file_metrics.append(report.metrics_df)
-                    file_reports.append(report)
-
-                    # Ensure all_metrics_dfs is initialized for next iteration
-                    if all_metrics_dfs is None:
-                        all_metrics_dfs = [report.metrics_df]
-
-                    # Track the absolute best report (highest score)
-                    if (
-                        best_report is None
-                        or report.best_score > best_report.best_score
-                    ):
-                        best_report = report
-
-                progress.advance(task)
-
-            # Process accumulated results for this file
-            if file_reports:
-                # Store metrics for plotting
-                metrics_by_file[(model, layer)] = file_metrics
-
-                # Aggregate results across runs (take mean and std)
-                best_sizes = [r.best_size for r in file_reports]
-                best_scores = [r.best_score for r in file_reports]
-                number_properties_list = [r.number_properties for r in file_reports]
-                hungarian_accuracies = [
-                    r.hungarian_accuracy
-                    for r in file_reports
-                    if r.hungarian_accuracy is not None
-                ]
-
-                avg_best_size = np.mean(best_sizes)
-                std_best_size = np.std(best_sizes) if len(best_sizes) > 1 else 0.0
-
-                avg_best_score = np.mean(best_scores)
-                std_best_score = np.std(best_scores) if len(best_scores) > 1 else 0.0
-
-                avg_number_properties = np.mean(number_properties_list)
-                std_number_properties = (
-                    np.std(number_properties_list)
-                    if len(number_properties_list) > 1
-                    else 0.0
-                )
+                for sample_idx in range(n_sample):
+                    status_parts = [
+                        f"[cyan]{model}[/cyan]/[yellow]{layer}[/yellow]",
+                        f"sample {sample_idx + 1}/{n_sample}",
+                    ]
+                    if best_overall_score is not None:
+                        status_parts.append(
+                            f"[green]Best: {best_overall_score:.3f}[/green] "
+                            f"([cyan]{best_overall_model}[/cyan]/[yellow]{best_overall_layer}[/yellow])"
+                        )
+                    if model in best_per_model:
+                        status_parts.append(
+                            f"[magenta]{model}: {best_per_model[model]:.3f}[/magenta]"
+                        )
+                    progress.update(task, description=" | ".join(status_parts))
 
-                avg_hungarian_accuracy = (
-                    np.mean(hungarian_accuracies) if hungarian_accuracies else None
-                )
-                std_hungarian_accuracy = (
-                    np.std(hungarian_accuracies)
-                    if len(hungarian_accuracies) > 1
-                    else 0.0
-                )
+                    try:
+                        report = estimate_model_clustering(
+                            transform_config=transform_config,
+                            optimization_config=optimization_config,
+                            file_path=file_path,
+                            selected_labels=selected_labels,
+                            all_metrics_dfs=all_metrics_dfs,
+                            embedding_read_status=lambda m, sp=status_parts: (
+                                progress.update(
+                                    task,
+                                    description=" | ".join([*sp, m]),
+                                )
+                            ),
+                        )
+                    except Exception as e:
+                        report = None
+                        console.print(
+                            "[yellow]Skipping failed sample[/yellow] "
+                            f"{model}/{layer} sample {sample_idx + 1}: {e}"
+                        )
 
-                results.append(
-                    {
-                        "model": model,
-                        "layer": layer,
-                        "best_size": avg_best_size,
-                        "best_size_std": std_best_size,
-                        "number_properties": avg_number_properties,
-                        "number_properties_std": std_number_properties,
-                        "best_score": avg_best_score,
-                        "best_score_std": std_best_score,
-                        "hungarian_accuracy": avg_hungarian_accuracy,
-                        "hungarian_accuracy_std": std_hungarian_accuracy,
-                    }
-                )
+                    if report is not None:
+                        file_metrics.append(report.metrics_df)
+                        file_reports.append(report)
+                        detailed_grid_frames.append(
+                            _metrics_df_with_grid_sample_columns(
+                                report,
+                                model=model,
+                                layer=layer,
+                                sample_idx=sample_idx,
+                            )
+                        )
+                        fine_metadata_frames.append(
+                            _fine_clustering_metadata_df(
+                                report,
+                                model=model,
+                                layer=layer,
+                                sample_idx=sample_idx,
+                            )
+                        )
+                        if (
+                            best_report is None
+                            or report.best_score > best_report.best_score
+                        ):
+                            best_report = report
+
+                    progress.advance(task)
+                    gc.collect()
+
+                if file_reports:
+                    metrics_by_file[(model, layer)] = file_metrics
+                    summary_row = summarize_clustering_reports_for_search(
+                        file_reports,
+                        model=model,
+                        layer=layer,
+                    )
+
+                    if len(file_metrics) > 1:
+                        plot_metrics_with_error_bars(
+                            file_metrics,
+                            output_dir / f"{model}_{layer}_error_bars.png",
+                        )
+                    else:
+                        plot_metrics(
+                            file_metrics[0], output_dir / f"{model}_{layer}.png"
+                        )
 
-                # Update best overall (using mean score)
-                if best_overall_score is None or avg_best_score > best_overall_score:
-                    best_overall_score = avg_best_score
-                    best_overall_model = model
-                    best_overall_layer = layer
-
-                # Update best per model
-                if (
-                    model not in best_per_model
-                    or avg_best_score > best_per_model[model]
-                ):
-                    best_per_model[model] = avg_best_score
-
-                if len(file_metrics) > 1:
-                    # Use lineplot with error bars for multiple samples
-                    plot_metrics_with_error_bars(
-                        file_metrics, output_dir / f"{model}_{layer}_error_bars.png"
+                    _mark_combination_done(
+                        ckpt,
+                        ckpt_path,
+                        combination_key=comb_key,
+                        summary=summary_row,
+                        singleton_score_key=comb_key,
                     )
+                    n_new = len(file_reports)
+                    _merge_new_frames_into_per_sample_grid_csv(
+                        detail_path,
+                        detailed_grid_frames[-n_new:],
+                    )
+                    _merge_new_frames_into_fine_metadata_pickle(
+                        fine_metadata_path,
+                        fine_metadata_frames[-n_new:],
+                    )
+                    completed.add(comb_key)
+                    results = _results_from_checkpoint(ckpt)
+                    (
+                        best_overall_score,
+                        best_overall_model,
+                        best_overall_layer,
+                        best_per_model,
+                    ) = _recompute_leaderboard_from_results(results)
                 else:
-                    # Use original plot for single sample
-                    plot_metrics(file_metrics[0], output_dir / f"{model}_{layer}.png")
+                    _record_failure(
+                        ckpt,
+                        ckpt_path,
+                        combination_key=comb_key,
+                        message="All samples failed for this combination",
+                    )
+
+        ckpt.stages["single"] = "complete"
+        save_checkpoint_atomic(ckpt_path, ckpt)
+
+    # --- fusion combinations ---
+    fusion_jobs: list[tuple[int, int]] = []
+    if run_fusion2 and fusion_pairs > 0:
+        fusion_jobs.append((2, fusion_pairs))
+    if run_fusion3 and fusion_triples > 0:
+        fusion_jobs.append((3, fusion_triples))
+
+    if fusion_pairs == 0:
+        ckpt.stages["fusion2"] = "skipped"
+    if fusion_triples == 0:
+        ckpt.stages["fusion3"] = "skipped"
+    if not fusion_jobs:
+        save_checkpoint_atomic(ckpt_path, ckpt)
 
-    # Create results dataframe
+    score_by_ml = _singleton_score_by_model_layer_from_checkpoint(ckpt)
+    singleton_items = singleton_items_by_dbcv_score(valid_files, score_by_ml)
+
+    fusion_batches: list[
+        tuple[
+            int,
+            int,
+            list[tuple[list[pathlib.Path], list[str], list[str], float]],
+        ]
+    ] = []
+    for order, top_k in fusion_jobs:
+        cand = top_k_fusion_candidates_by_dbcv_proxy(singleton_items, order, top_k)
+        if not cand:
+            continue
+        fusion_batches.append((order, top_k, cand))
+
+    handled_fusion_orders: set[int] = set()
+    for order, _top_k, candidates in fusion_batches:
+        handled_fusion_orders.add(order)
+        model_label = f"fusion{order}"
+        stage_name = "fusion2" if order == 2 else "fusion3"
+        fusion_task_total = 0
+        for paths, models, layers, _sum_proxy in candidates:
+            ckey = combination_key_from_members(
+                list(zip(models, layers, strict=True)),
+            )
+            if ckey not in completed:
+                fusion_task_total += n_sample
+
+        if fusion_task_total > 0:
+            console.print(
+                "[cyan]Fused embeddings:[/cyan] evaluating remaining combinations "
+                f"× {n_sample} sample(s)..."
+            )
+
+        ckpt.stages[stage_name] = "in_progress"
+        save_checkpoint_atomic(ckpt_path, ckpt)
+
+        if fusion_task_total > 0:
+            with Progress(
+                SpinnerColumn(),
+                TextColumn("[bold blue]{task.description}"),
+                BarColumn(),
+                TextColumn("[progress.percentage]{task.percentage:>3.0f}%"),
+                TimeElapsedColumn(),
+                console=console,
+                refresh_per_second=4,
+            ) as fusion_progress:
+                ftask = fusion_progress.add_task(
+                    "[cyan]Fusion clustering...",
+                    total=fusion_task_total,
+                )
+                for paths, models, layers, sum_proxy in candidates:
+                    ordered = sorted(
+                        zip(paths, models, layers, strict=True),
+                        key=lambda t: (t[1], t[2]),
+                    )
+                    ordered_paths = [t[0] for t in ordered]
+                    o_models = [t[1] for t in ordered]
+                    o_layers = [t[2] for t in ordered]
+                    layer_label = "+".join(
+                        f"{m}/{lyr}" for m, lyr in zip(o_models, o_layers, strict=True)
+                    )
+                    comb_key = combination_key_from_members(
+                        list(zip(o_models, o_layers, strict=True)),
+                    )
+
+                    if comb_key in completed:
+                        fusion_progress.advance(ftask, advance=n_sample)
+                        continue
+
+                    fusion_metrics: list[pd.DataFrame] = []
+                    fusion_reports: list[ClusteringReport] = []
+                    fusion_all_metrics_dfs: list[pd.DataFrame] = []
+
+                    optimization_config = ClusteringOptimizationConfig(
+                        min_class_size=min_class_size,
+                        max_scale=max_scale,
+                        rns=RandomState(seed=seed),
+                        frac=frac,
+                        n_embedding_batches=n_embedding_batches,
+                        batch_size=batch_size,
+                        optimization_method="mean",
+                    )
+
+                    for sample_idx in range(n_sample):
+                        fusion_status = (
+                            f"[cyan]{model_label}[/cyan] "
+                            f"[yellow]{layer_label}[/yellow] "
+                            f"(Σ singles≈{sum_proxy:.3f}) "
+                            f"sample {sample_idx + 1}/{n_sample}"
+                        )
+                        fusion_progress.update(ftask, description=fusion_status)
+                        try:
+                            report = estimate_model_clustering(
+                                transform_config=transform_config,
+                                optimization_config=optimization_config,
+                                file_paths=ordered_paths,
+                                selected_labels=selected_labels,
+                                all_metrics_dfs=fusion_all_metrics_dfs,
+                                embedding_read_status=lambda m, fs=fusion_status: (
+                                    fusion_progress.update(
+                                        ftask,
+                                        description=f"{fs} | [dim]{m}[/dim]",
+                                    )
+                                ),
+                            )
+                        except Exception as e:
+                            report = None
+                            console.print(
+                                "[yellow]Skipping failed fusion sample[/yellow] "
+                                f"{layer_label} sample {sample_idx + 1}: {e}"
+                            )
+                        if report is not None:
+                            fusion_metrics.append(report.metrics_df)
+                            fusion_reports.append(report)
+                            detailed_grid_frames.append(
+                                _metrics_df_with_grid_sample_columns(
+                                    report,
+                                    model=model_label,
+                                    layer=layer_label,
+                                    sample_idx=sample_idx,
+                                )
+                            )
+                            fine_metadata_frames.append(
+                                _fine_clustering_metadata_df(
+                                    report,
+                                    model=model_label,
+                                    layer=layer_label,
+                                    sample_idx=sample_idx,
+                                )
+                            )
+                            if (
+                                best_report is None
+                                or report.best_score > best_report.best_score
+                            ):
+                                best_report = report
+                        fusion_progress.advance(ftask)
+                        gc.collect()
+
+                    if fusion_reports:
+                        fusion_summary = summarize_clustering_reports_for_search(
+                            fusion_reports,
+                            model=model_label,
+                            layer=layer_label,
+                        )
+                        metrics_by_file[(model_label, layer_label)] = fusion_metrics
+
+                        safe = layer_label.replace("/", "_").replace("+", "__")
+                        out_metric = output_dir / f"{model_label}_{safe}.png"
+                        if len(fusion_metrics) > 1:
+                            plot_metrics_with_error_bars(fusion_metrics, out_metric)
+                        else:
+                            plot_metrics(fusion_metrics[0], out_metric)
+
+                        _mark_combination_done(
+                            ckpt,
+                            ckpt_path,
+                            combination_key=comb_key,
+                            summary=fusion_summary,
+                            singleton_score_key=None,
+                        )
+                        n_new = len(fusion_reports)
+                        _merge_new_frames_into_per_sample_grid_csv(
+                            detail_path,
+                            detailed_grid_frames[-n_new:],
+                        )
+                        _merge_new_frames_into_fine_metadata_pickle(
+                            fine_metadata_path,
+                            fine_metadata_frames[-n_new:],
+                        )
+                        completed.add(comb_key)
+                        results = _results_from_checkpoint(ckpt)
+                    else:
+                        _record_failure(
+                            ckpt,
+                            ckpt_path,
+                            combination_key=comb_key,
+                            message="All fusion samples failed for this combination",
+                        )
+
+        ckpt.stages[stage_name] = "complete"
+        save_checkpoint_atomic(ckpt_path, ckpt)
+
+    for order, _top_k in fusion_jobs:
+        if order in handled_fusion_orders:
+            continue
+        stage_name = "fusion2" if order == 2 else "fusion3"
+        ckpt.stages[stage_name] = "complete"
+    if fusion_jobs:
+        save_checkpoint_atomic(ckpt_path, ckpt)
+
+    results = _results_from_checkpoint(ckpt)
     if not results:
         console.print("[red]No results to save[/red]")
         return
 
-    df_results = pd.DataFrame(results)
+    df_results = pd.DataFrame([r.to_flat_dict() for r in results])
     df_results = df_results.sort_values(["model", "layer"])
-
-    # Save results
-    output_path = output_dir / "results.csv"
     df_results.to_csv(output_path, index=False)
 
-    # Create heatmaps
+    df_grid_detail = _read_optional_csv(detail_path)
+    if df_grid_detail is not None and not df_grid_detail.empty:
+        df_grid_detail = _dedupe_per_sample_grid(df_grid_detail)
+        tmp_grid = detail_path.with_suffix(detail_path.suffix + ".tmp")
+        df_grid_detail.to_csv(tmp_grid, index=False)
+        tmp_grid.replace(detail_path)
+        scatter_path = output_dir / "model.dbcv_vs_ari.png"
+        if plot_dbcv_vs_ari_from_grid(df_grid_detail, scatter_path):
+            console.print(
+                f"[green]✓[/green] DBCV vs ARI scatter saved to: "
+                f"[cyan]{scatter_path}[/cyan]"
+            )
+
+    df_heatmap = df_results[~df_results["model"].isin(["fusion2", "fusion3"])].copy()
+
     heatmap_path = output_dir / "model.perf.heatmap.png"
-    plot_heatmap(
-        df_results, heatmap_path, metric="best_score", metric_label="Best Score"
-    )
+    if len(df_heatmap) > 0:
+        plot_heatmap(
+            df_heatmap, heatmap_path, metric="best_score", metric_label="Best Score"
+        )
+    else:
+        console.print(
+            "[yellow]Skipping score heatmap: no single-embedding rows[/yellow]"
+        )
 
-    # Create Hungarian accuracy heatmap if available
     if (
-        "hungarian_accuracy" in df_results.columns
-        and df_results["hungarian_accuracy"].notna().any()
+        len(df_heatmap) > 0
+        and "ari" in df_heatmap.columns
+        and df_heatmap["ari"].notna().any()
     ):
-        hungarian_heatmap_path = output_dir / "model.hungarian_accuracy.heatmap.png"
+        ari_heatmap_path = output_dir / "model.ari.heatmap.png"
         plot_heatmap(
-            df_results,
-            hungarian_heatmap_path,
-            metric="hungarian_accuracy",
-            metric_label="Hungarian Accuracy",
+            df_heatmap,
+            ari_heatmap_path,
+            metric="ari",
+            metric_label="ARI",
         )
 
     console.print("\n[bold green]Results Summary[/bold green]")
@@ -394,19 +1112,24 @@ def main(
     table.add_column("Model", style="cyan")
     table.add_column("Layer", style="yellow")
     table.add_column("Best Size", justify="right", style="green")
+    table.add_column("Clusters", justify="right", style="bright_blue")
     table.add_column("Properties", justify="right", style="magenta")
     table.add_column("Best Score", justify="right", style="blue")
 
     for _, row in df_results.iterrows():
-        # Format with std if n_sample > 1
         if n_sample > 1:
             best_size_str = f"{int(row['best_size'])} ± {row['best_size_std']:.1f}"
+            clusters_str = (
+                f"{row['n_clusters_emergent']:.1f} ± "
+                f"{row['n_clusters_emergent_std']:.1f}"
+            )
             properties_str = (
                 f"{int(row['number_properties'])} ± {row['number_properties_std']:.1f}"
             )
             best_score_str = f"{row['best_score']:.3f} ± {row['best_score_std']:.3f}"
         else:
             best_size_str = str(int(row["best_size"]))
+            clusters_str = str(int(round(row["n_clusters_emergent"])))
             properties_str = str(int(row["number_properties"]))
             best_score_str = f"{row['best_score']:.3f}"
 
@@ -414,21 +1137,63 @@ def main(
             str(row["model"]),
             str(row["layer"]),
             best_size_str,
+            clusters_str,
             properties_str,
             best_score_str,
         )
 
     console.print(table)
     console.print(f"\n[green]✓[/green] Results saved to: [cyan]{output_path}[/cyan]")
-    console.print(f"[green]✓[/green] Heatmap saved to: [cyan]{heatmap_path}[/cyan]")
+    if df_grid_detail is not None and not df_grid_detail.empty:
+        console.print(
+            f"[green]✓[/green] Per-sample grid (all min_cluster_size values) saved to: "
+            f"[cyan]{detail_path}[/cyan]"
+        )
+    if fine_metadata_path.exists():
+        try:
+            fm = pd.read_pickle(fine_metadata_path, compression="gzip")
+        except Exception:
+            fm = None
+        if fm is not None and not fm.empty:
+            console.print(
+                f"[green]✓[/green] Fine clustering metadata (gzipped pickle) saved to: "
+                f"[cyan]{fine_metadata_path}[/cyan]"
+            )
+    if len(df_heatmap) > 0:
+        console.print(f"[green]✓[/green] Heatmap saved to: [cyan]{heatmap_path}[/cyan]")
+
+    top_idx = df_results["best_score"].idxmax()
+    top_row = df_results.loc[top_idx]
+    top_summary = clustering_search_summary_row_from_flat_dict(
+        {str(k): top_row[k] for k in top_row.index}
+    )
+    console.print(
+        f"\n[bold green]Best mean DBCV (best_score): {float(top_row['best_score']):.3f}[/bold green] "
+        f"([cyan]{top_row['model']}[/cyan]/[yellow]{top_row['layer']}[/yellow])"
+    )
 
-    if best_overall_score is not None:
+    if best_report is None:
+        viz_config = ClusteringOptimizationConfig(
+            min_class_size=min_class_size,
+            max_scale=max_scale,
+            rns=RandomState(seed=seed),
+            frac=frac,
+            n_embedding_batches=n_embedding_batches,
+            batch_size=batch_size,
+            optimization_method="mean",
+        )
         console.print(
-            f"\n[bold green]Best overall score: {best_overall_score:.3f}[/bold green] "
-            f"([cyan]{best_overall_model}[/cyan]/[yellow]{best_overall_layer}[/yellow])"
+            "[cyan]Materializing best clustering report for UMAP (not held in memory)...[/cyan]"
+        )
+        best_report = _materialize_best_report(
+            top_summary,
+            valid_files=valid_files,
+            path_by_ml=path_by_ml,
+            transform_config=transform_config,
+            optimization_config=viz_config,
+            selected_labels=selected_labels,
         )
 
-    # Generate UMAP visualization for the best model
     if best_report is not None:
         umap_viz_path = output_dir / "umap_best.html"
         console.print(
diff --git a/run/analysis/replot_dbcv_ari_scatter.py b/run/analysis/replot_dbcv_ari_scatter.py
new file mode 100644
index 0000000..5c78020
--- /dev/null
+++ b/run/analysis/replot_dbcv_ari_scatter.py
@@ -0,0 +1,41 @@
+"""Replot DBCV vs ARI scatter from an existing ``results_grid_per_sample.csv``."""
+
+from __future__ import annotations
+
+import pathlib
+import sys
+
+import click
+import pandas as pd
+
+from pelinker.plotting import plot_dbcv_vs_ari_from_grid
+
+
+@click.command()
+@click.argument(
+    "grid_csv",
+    type=click.Path(path_type=pathlib.Path, exists=True, dir_okay=False),
+)
+@click.option(
+    "--output",
+    "-o",
+    type=click.Path(path_type=pathlib.Path),
+    default=None,
+    help="Output PNG path (default: alongside CSV as model.dbcv_vs_ari.png)",
+)
+def main(grid_csv: pathlib.Path, output: pathlib.Path | None) -> None:
+    df = pd.read_csv(grid_csv)
+    out = output if output is not None else grid_csv.parent / "model.dbcv_vs_ari.png"
+    if not plot_dbcv_vs_ari_from_grid(df, out):
+        click.echo(
+            "Could not build figure (need model, layer, sample_idx, "
+            "sample_best_dbcv, sample_ari with at least one "
+            "non-null ARI row).",
+            err=True,
+        )
+        sys.exit(1)
+    click.echo(f"Wrote {out}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/run/embed_kb_corpus.py b/run/embed_kb_corpus.py
index 48caa84..111aaa5 100644
--- a/run/embed_kb_corpus.py
+++ b/run/embed_kb_corpus.py
@@ -2,6 +2,7 @@
 import pathlib
 import logging
 
+from pelinker.config import EmbeddingModelMetadata, EmbeddingTrainingConfig
 from pelinker.embedder import embed_kb_corpus
 
 logger = logging.getLogger(__name__)
@@ -11,8 +12,8 @@
 @click.option(
     "--model-type",
     type=click.STRING,
-    default="biobert",
-    help="Backbone model type for token embeddings.",
+    default="pubmedbert",
+    help="Backbone model type for token embeddings (same vocabulary as pelinker.cli.fit).",
 )
 @click.option(
     "--layers-spec",
@@ -44,16 +45,18 @@
     help="Enable GPU acceleration if CUDA is available",
 )
 @click.option(
-    "--chunk-size",
+    "--input-buffer-rows",
+    "input_buffer_rows",
     type=click.INT,
     default=1000,
-    help="Chunk size for streaming input. Each chunk is split into batches and serialized.",
+    help="Rows per pandas read pass over the text table (I/O only; not encoder batch size).",
 )
 @click.option(
-    "--batch-size",
+    "--encoder-batch-size",
+    "encoder_batch_size",
     type=click.INT,
     default=200,
-    help="Embedding batch size inside a chunk.",
+    help="Table rows per transformer encoder forward pass (lower if GPU runs out of memory).",
 )
 @click.option(
     "--nlp-model",
@@ -62,10 +65,11 @@
     help="spaCy model to use for tokenization/lemmas.",
 )
 @click.option(
-    "--head",
+    "--max-input-buffers",
+    "max_input_buffers",
     type=click.INT,
     default=None,
-    help="Number of chunks to process (skip it for all chunks).",
+    help="Stop after this many text-table read passes (each up to --input-buffer-rows rows).",
 )
 def run(
     model_type,
@@ -74,28 +78,31 @@ def run(
     kb_csv_path,
     output_parquet_path,
     use_gpu,
-    chunk_size,
-    batch_size,
+    input_buffer_rows,
+    encoder_batch_size,
     nlp_model,
-    head,
+    max_input_buffers,
 ):
     # Set up logging
     logging.basicConfig(
         level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
     )
 
-    # Call the embedding function
-    embed_kb_corpus(
-        model_type=model_type,
-        layers_spec=layers_spec,
+    metadata = EmbeddingModelMetadata.from_single(model_type, layers_spec)
+    training = EmbeddingTrainingConfig(
         input_text_table_path=input_text_table_path,
         kb_csv_path=kb_csv_path,
-        output_parquet_path=output_parquet_path,
         use_gpu=use_gpu,
-        chunk_size=chunk_size,
-        batch_size=batch_size,
+        input_buffer_rows=input_buffer_rows,
+        encoder_batch_size=encoder_batch_size,
         nlp_model=nlp_model,
-        head=head,
+        max_input_buffers=max_input_buffers,
+    )
+
+    embed_kb_corpus(
+        metadata=metadata,
+        training=training,
+        output_parquet_path=output_parquet_path,
     )
 
 
diff --git a/run/loop.fit.sh b/run/loop.fit.sh
new file mode 100755
index 0000000..277b507
--- /dev/null
+++ b/run/loop.fit.sh
@@ -0,0 +1,112 @@
+#!/usr/bin/env bash
+#
+# Batch end-to-end linker training: same (model_type × layers) grid as loop.embed.kb.corpus.sh,
+# but runs pelinker-fit (embed_kb_corpus stage A + Linker.fit stage B) per combination.
+#
+# From the repository root:
+#   ./run/loop.fit.sh \
+#     --input-text-table-path data/corpus/articles.tsv.gz \
+#     --kb-csv-path data/derived/properties.synthesis.1.csv \
+#     --output-parquet-prefix outputs/embeddings \
+#     --output-model-prefix models/fit_runs \
+#     [--layers 1,2,3]
+
+set -euo pipefail
+
+mtypes=("bert" "biobert" "pubmedbert" "bluebert" "scibert")
+# Layer specs for pelinker-fit (each becomes layers_spec=...).
+# - Comma-separated: "1,2,3" → three runs (1, 2, 3).
+# - Use semicolons to pass comma-containing specs: "1,2;3" → two runs (1,2 and 3).
+LAYERS_RAW="1,2,3"
+
+INPUT_TEXT_TABLE_PATH=""
+OUTPUT_PARQUET_PREFIX=""
+OUTPUT_MODEL_PREFIX=""
+KB_CSV_PATH=""
+
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --input-text-table-path)
+            INPUT_TEXT_TABLE_PATH="$2"
+            shift 2
+            ;;
+        --output-parquet-prefix)
+            OUTPUT_PARQUET_PREFIX="$2"
+            shift 2
+            ;;
+        --output-model-prefix)
+            OUTPUT_MODEL_PREFIX="$2"
+            shift 2
+            ;;
+        --kb-csv-path)
+            KB_CSV_PATH="$2"
+            shift 2
+            ;;
+        --layers)
+            LAYERS_RAW="$2"
+            shift 2
+            ;;
+        *)
+            echo "Unknown option: $1"
+            echo "Usage: $0 --input-text-table-path PATH --kb-csv-path PATH \\"
+            echo "          --output-parquet-prefix DIR --output-model-prefix DIR \\"
+            echo "          [--layers SPECS]"
+            echo "  --layers: layer specs (default: 1,2,3). Comma = separate specs;"
+            echo "            use semicolons for one spec that contains commas, e.g. 1,2;3"
+            exit 1
+            ;;
+    esac
+done
+
+if [[ "$LAYERS_RAW" == *';'* ]]; then
+    IFS=';' read -r -a layers <<< "$LAYERS_RAW"
+else
+    IFS=',' read -r -a layers <<< "$LAYERS_RAW"
+fi
+if [[ ${#layers[@]} -eq 0 ]] || [[ -z "${layers[0]}" ]]; then
+    echo "Error: --layers must list at least one layers_spec"
+    exit 1
+fi
+
+if [[ -z "$INPUT_TEXT_TABLE_PATH" ]]; then
+    echo "Error: --input-text-table-path is required"
+    exit 1
+fi
+
+if [[ -z "$OUTPUT_PARQUET_PREFIX" ]]; then
+    echo "Error: --output-parquet-prefix is required"
+    exit 1
+fi
+
+if [[ -z "$OUTPUT_MODEL_PREFIX" ]]; then
+    echo "Error: --output-model-prefix is required"
+    exit 1
+fi
+
+if [[ -z "$KB_CSV_PATH" ]]; then
+    echo "Error: --kb-csv-path is required"
+    exit 1
+fi
+
+mkdir -p "$OUTPUT_PARQUET_PREFIX" "$OUTPUT_MODEL_PREFIX"
+
+for model in "${mtypes[@]}"; do
+    for layer in "${layers[@]}"; do
+        layer_trimmed="${layer#"${layer%%[![:space:]]*}"}"
+        layer_trimmed="${layer_trimmed%"${layer_trimmed##*[![:space:]]}"}"
+        layer_tag="${layer_trimmed//,/_}"
+        emb_file="${OUTPUT_PARQUET_PREFIX}/res_${model}_${layer_tag}.parquet"
+        model_file="${OUTPUT_MODEL_PREFIX}/pelinker.${model}.${layer_tag}"
+        uv run pelinker-fit \
+            kb_path="$KB_CSV_PATH" \
+            input_text_table_path="$INPUT_TEXT_TABLE_PATH" \
+            embeddings_parquet="$emb_file" \
+            output_path="$model_file" \
+            model_type="$model" \
+            layers_spec="$layer_trimmed" \
+            nlp_model=en_core_web_trf \
+            encoder_batch_size=100 \
+            input_buffer_rows=2000 \
+            use_gpu=true
+    done
+done
diff --git a/run/obsolete/testing/find_occurences.py b/run/obsolete/testing/find_occurences.py
deleted file mode 100644
index c50a81f..0000000
--- a/run/obsolete/testing/find_occurences.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# pylint: disable=E1120
-
-import json
-import logging.config
-import sys
-import spacy
-
-import click
-import pandas as pd
-
-from pelinker.matching import match_pattern, match_pieces
-from pelinker.util import fetch_latest_kb, get_vb_spans, split_into_sentences
-from pathlib import Path
-
-
-@click.command()
-@click.option("--text-path", type=click.Path())
-def run(text_path):
-    suffix_length = 1
-    with open(text_path) as json_file:
-        json_data = json.load(json_file)
-    text = json_data["text"]
-    path_derived = Path("./data/derived/")
-
-    fname, version = fetch_latest_kb(path_derived)
-
-    try:
-        df0 = pd.read_csv(path_derived / fname)
-    except Exception as e:
-        print(f"kb not found at {path_derived}")
-        raise e
-
-    nlp = spacy.load("en_core_web_trf")
-
-    patterns = df0["label"].values[:]
-
-    texts = split_into_sentences(text)
-
-    for text in texts:
-        word_bnds = get_vb_spans(nlp=nlp, text=text, extra_context=False)
-        for pattern in patterns:
-            matches = match_pattern(pattern, text, suffix_length=1)
-            if matches:
-                vb_match = [
-                    (a, b)
-                    for a, b in matches
-                    if any(min([y, b]) - max([x, a]) > 0 for x, y in word_bnds)
-                ]
-                if vb_match:
-                    wmaps = match_pieces(
-                        pattern, text, suffix_length=suffix_length, matches=matches
-                    )
-                    print(pattern, wmaps)
-
-
-if __name__ == "__main__":
-    logging.basicConfig(level=logging.INFO, stream=sys.stdout)
-    run()
diff --git a/run/obsolete/testing/run_pel_test.py b/run/obsolete/testing/run_pel_test.py
deleted file mode 100644
index f278bf1..0000000
--- a/run/obsolete/testing/run_pel_test.py
+++ /dev/null
@@ -1,119 +0,0 @@
-# pylint: disable=E1120
-
-import json
-import logging.config
-import sys
-
-import click
-import pandas as pd
-import spacy
-
-from pelinker.util import (
-    load_models,
-)
-from pelinker.onto import MAX_LENGTH
-from pelinker.preprocess import pre_process_properties
-from pelinker.model import Linker
-from pelinker.util import fetch_latest_kb, report2kb, texts_to_vrep
-from pelinker.onto import WordGrouping
-from pathlib import Path
-
-
-@click.command()
-@click.option("--text-path", type=click.Path())
-@click.option("--model-type", type=click.STRING, default="scibert")
-@click.option("--extra-context", type=click.BOOL, is_flag=True, default=False)
-@click.option(
-    "--superposition",
-    type=click.BOOL,
-    default=False,
-    is_flag=True,
-    help="use a superposition of label and description embeddings, where available",
-)
-@click.option(
-    "--layers-spec",
-    type=click.STRING,
-    default="1,2,3",
-    help="`sent` or a string of layers, `1,2,3` would correspond to layers [-1, -2, -3]",
-)
-def run(text_path, model_type, superposition, extra_context, layers_spec):
-    superposition_sfx = "_superposition" if superposition else ""
-    suffix = f"_ctx_{'extra' if extra_context else 'vb'}{superposition_sfx}"
-    report_path = "./reports"
-    with open(text_path) as json_file:
-        json_data = json.load(json_file)
-    text = json_data["text"]
-    gt = json_data["ground_truth"]
-
-    path_derived = Path("./data/derived/")
-
-    fname, version = fetch_latest_kb(path_derived)
-
-    try:
-        df0 = pd.read_csv(path_derived / fname)
-    except Exception as e:
-        print(f"kb not found at {path_derived}")
-        raise e
-
-    layers = Linker.str2layers(layers_spec)
-    sentence = True if layers == "sent" else False
-
-    report = pre_process_properties(df0)
-    labels = report.pop("labels")
-    properties = report.pop("entity_ids")
-    property_label_map = report.pop("property_label_map")
-
-    nlp = spacy.load("en_core_web_trf")
-
-    tokenizer, model = load_models(model_type, sentence)
-
-    layers_str = Linker.layers2str(layers)
-
-    report = texts_to_vrep(
-        labels,
-        tokenizer,
-        model,
-        layers_spec=layers,
-        word_modes=[WordGrouping.W1],
-        nlp=nlp,
-    )
-    vocabulary, index = report2kb(report, wg=WordGrouping.W1)
-
-    nb_nn = min([10, report["tensor"].shape[0]])
-    lm = Linker(
-        index=index,
-        vocabulary=properties,
-        layers=layers,
-        labels_map=property_label_map,
-        nb_nn=nb_nn,
-    )
-    report = lm.predict(
-        [text],
-        tokenizer,
-        model,
-        nlp,
-        max_length=MAX_LENGTH,
-        topk=3,
-    )
-
-    df_gt = pd.DataFrame(gt)
-
-    df_pred = pd.DataFrame(report["entities"])
-    df_gt.merge(df_pred, how="left", on=["itext", "a"], suffixes=("", "_gt"))
-    df_cmp = df_gt.merge(df_pred, how="left", on=["itext", "a"], suffixes=("", "_gt"))
-
-    accuracy = (
-        (df_cmp["entity_id"] == df_cmp["entity_id_predicted"]).astype(float).mean()
-    )
-    accuracy = round(accuracy, 5)
-    print(f"{model_type} | {layers_str} | {suffix} : accuracy >> {accuracy} <<")
-    report = {"accuracy": accuracy, "prediction": df_cmp.to_dict(orient="records")}
-    with open(
-        f"{report_path}/metrics_{model_type}_{layers_str}{suffix}.json", "w"
-    ) as file:
-        json.dump(report, file, indent=4)
-
-
-if __name__ == "__main__":
-    logging.basicConfig(level=logging.INFO, stream=sys.stdout)
-    run()
diff --git a/run_test.sh b/run_test.sh
deleted file mode 100755
index 9209efd..0000000
--- a/run_test.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-
-mt=pubmedbert
-fpath=$1
-
-python -m run.testing.run_pel_test --text-path $1 --model-type $mt
-python -m run.testing.run_pel_test --text-path $1 --model-type $mt --extra-context
-python -m run.testing.run_pel_test --text-path $1 --model-type $mt --superposition
-python -m run.testing.run_pel_test --text-path $1 --model-type $mt --superposition --extra-context
\ No newline at end of file
diff --git a/test/test_clustering_fusion_ranking.py b/test/test_clustering_fusion_ranking.py
new file mode 100644
index 0000000..0f3249f
--- /dev/null
+++ b/test/test_clustering_fusion_ranking.py
@@ -0,0 +1,36 @@
+"""Tests for fusion candidate ranking helpers."""
+
+import pathlib
+
+import pytest
+
+from pelinker.clustering_fusion_ranking import (
+    singleton_items_by_dbcv_score,
+    top_k_fusion_candidates_by_dbcv_proxy,
+)
+
+
+def test_singleton_items_by_dbcv_score_sorts_desc() -> None:
+    files = [
+        (pathlib.Path("/a"), "m1", "L0"),
+        (pathlib.Path("/b"), "m2", "L1"),
+    ]
+    scores = {("m1", "L0"): 0.5, ("m2", "L1"): 0.9}
+    rows = singleton_items_by_dbcv_score(files, scores)
+    assert [t[3] for t in rows] == [0.9, 0.5]
+
+
+def test_top_k_fusion_candidates_deduplicates_component_sets() -> None:
+    items = [
+        (pathlib.Path("/a"), "m1", "L0", 0.8),
+        (pathlib.Path("/b"), "m2", "L1", 0.7),
+        (pathlib.Path("/c"), "m3", "L2", 0.1),
+    ]
+    # Best pair sum: m1/L0 + m2/L1 = 1.5
+    out = top_k_fusion_candidates_by_dbcv_proxy(items, order=2, k=2)
+    assert len(out) >= 1
+    assert out[0][3] == pytest.approx(1.5)
+
+
+def test_top_k_fusion_candidates_short_circuit() -> None:
+    assert top_k_fusion_candidates_by_dbcv_proxy([], order=2, k=1) == []
diff --git a/test/test_clustering_grid_solver.py b/test/test_clustering_grid_solver.py
new file mode 100644
index 0000000..faa37a2
--- /dev/null
+++ b/test/test_clustering_grid_solver.py
@@ -0,0 +1,194 @@
+"""Tests for typed grid aggregation and smoothed min_cluster_size selection."""
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from pelinker.clustering_grid import (
+    AggregatedGridPoint,
+    AggregatedGridReport,
+    ScalarMetricAggregate,
+    aggregate_grid_metrics,
+    aggregated_grid_report_to_dataframe,
+    solve_optimal_min_cluster_size_from_aggregated,
+)
+
+
+def _report_from_arrays(
+    sizes: list[int],
+    means: list[float],
+    stds: list[float],
+    counts: list[int],
+) -> AggregatedGridReport:
+    points = [
+        AggregatedGridPoint(
+            min_cluster_size=s,
+            dbcv=ScalarMetricAggregate(mean=m, std=sd, count=c),
+            icm_mean=float("nan"),
+            n_clusters_mean=float("nan"),
+        )
+        for s, m, sd, c in zip(sizes, means, stds, counts, strict=True)
+    ]
+    return AggregatedGridReport(points=tuple(points))
+
+
+def test_aggregate_grid_metrics_empty() -> None:
+    r = aggregate_grid_metrics([])
+    assert r.points == ()
+
+
+def test_aggregate_grid_metrics_preserves_std_and_count() -> None:
+    a = pd.DataFrame(
+        {
+            "min_cluster_size": [10, 20],
+            "icm": [0.1, 0.2],
+            "n_clusters": [3, 4],
+            "dbcv": [0.5, 0.6],
+        }
+    )
+    b = pd.DataFrame(
+        {
+            "min_cluster_size": [10, 20],
+            "icm": [0.15, 0.25],
+            "n_clusters": [3, 5],
+            "dbcv": [0.7, 0.4],
+        }
+    )
+    r = aggregate_grid_metrics([a, b])
+    assert len(r.points) == 2
+    p10 = next(p for p in r.points if p.min_cluster_size == 10)
+    assert p10.dbcv.mean == pytest.approx(0.6)
+    assert p10.dbcv.count == 2
+    assert p10.dbcv.std == pytest.approx(np.std([0.5, 0.7], ddof=1))
+
+
+def test_aggregated_grid_report_to_dataframe_columns() -> None:
+    r = _report_from_arrays([5, 10], [1.0, 2.0], [0.1, 0.2], [3, 4])
+    df = aggregated_grid_report_to_dataframe(r)
+    assert list(df.columns) == [
+        "min_cluster_size",
+        "dbcv_mean",
+        "dbcv_std",
+        "dbcv_count",
+        "icm_mean",
+        "n_clusters_mean",
+    ]
+    assert df.iloc[0]["dbcv_count"] == 3
+
+
+def test_aggregated_grid_report_rejects_unsorted_points() -> None:
+    p1 = AggregatedGridPoint(
+        10,
+        ScalarMetricAggregate(1.0, 0.0, 1),
+        0.0,
+        0.0,
+    )
+    p2 = AggregatedGridPoint(
+        5,
+        ScalarMetricAggregate(1.0, 0.0, 1),
+        0.0,
+        0.0,
+    )
+    with pytest.raises(ValueError, match="sorted"):
+        AggregatedGridReport(points=(p1, p2))
+
+
+def test_solve_empty_report_raises() -> None:
+    with pytest.raises(ValueError, match="No aggregated"):
+        solve_optimal_min_cluster_size_from_aggregated(AggregatedGridReport(points=()))
+
+
+def test_solve_plateau_prefers_leftmost_high_flat_region() -> None:
+    """Sharp rise then flat top: derivative small on the right; pick early plateau x."""
+    sizes = [10, 15, 20, 25, 30, 35, 40]
+    means = [0.1, 0.2, 0.5, 1.5, 2.0, 2.01, 2.0]
+    stds = [0.05] * len(sizes)
+    counts = [10] * len(sizes)
+    r = _report_from_arrays(sizes, means, stds, counts)
+    out = solve_optimal_min_cluster_size_from_aggregated(
+        r,
+        method="mean",
+        smooth_window=3,
+        plateau_fraction=0.9,
+        derivative_rel_tol=0.2,
+        precision_weighted_smooth=False,
+    )
+    assert out.selection == "plateau_derivative"
+    assert out.chosen_min_cluster_size in {25, 30, 35}
+    assert out.score_mean_at_chosen == pytest.approx(
+        means[sizes.index(out.chosen_min_cluster_size)]
+    )
+
+
+def test_solve_smoothed_argmax_when_no_plateau() -> None:
+    """Strictly increasing objective: derivative stays positive → fall back to smoothed peak."""
+    sizes = [10, 15, 20, 25, 30]
+    means = [0.1, 0.4, 0.7, 1.0, 1.3]
+    r = _report_from_arrays(sizes, means, [0.01] * 5, [5] * 5)
+    out = solve_optimal_min_cluster_size_from_aggregated(
+        r,
+        method="mean",
+        smooth_window=3,
+        plateau_fraction=0.999,
+        derivative_rel_tol=1e-9,
+        precision_weighted_smooth=False,
+    )
+    assert out.selection == "smoothed_argmax"
+    assert out.chosen_min_cluster_size == 30
+
+
+def test_solve_lower_bound_objective() -> None:
+    sizes = [10, 20]
+    means = [0.2, 1.0]
+    stds = [0.05, 0.01]
+    r = _report_from_arrays(sizes, means, stds, [5, 5])
+    out = solve_optimal_min_cluster_size_from_aggregated(
+        r,
+        method="lower_bound",
+        uncertainty_penalty=1.0,
+        smooth_window=1,
+        plateau_fraction=0.9,
+        derivative_rel_tol=1.0,
+        precision_weighted_smooth=False,
+    )
+    assert out.chosen_min_cluster_size == 20
+
+
+def test_solve_unknown_method_raises() -> None:
+    r = _report_from_arrays([10], [1.0], [0.0], [1])
+    with pytest.raises(ValueError, match="Unknown optimization method"):
+        solve_optimal_min_cluster_size_from_aggregated(r, method="nope")
+
+
+def test_user_style_noisy_sequence_reasonable_choice() -> None:
+    """Noisy scores that rise then wiggle near a ceiling (similar to user example)."""
+    sizes = list(range(10, 10 + 8 * 5, 5))
+    means = [0.9, 0.8, 1.6, 2.2, 2.4, 2.3, 2.5, 2.6]
+    stds = [0.15] * len(sizes)
+    counts = [8] * len(sizes)
+    r = _report_from_arrays(sizes, means, stds, counts)
+    out = solve_optimal_min_cluster_size_from_aggregated(
+        r,
+        method="mean",
+        smooth_window=3,
+        plateau_fraction=0.88,
+        derivative_rel_tol=0.25,
+        precision_weighted_smooth=True,
+    )
+    assert out.chosen_min_cluster_size in set(sizes)
+    assert out.score_mean_at_chosen in means
+    assert len(out.dy_dx) == len(out.x)
+
+
+def test_finite_mask_drops_non_finite_objective() -> None:
+    sizes = [10, 15, 20]
+    means = [float("nan"), 1.0, 2.0]
+    r = _report_from_arrays(sizes, means, [0.0, 0.0, 0.0], [1, 1, 1])
+    out = solve_optimal_min_cluster_size_from_aggregated(
+        r,
+        smooth_window=1,
+        plateau_fraction=0.5,
+        derivative_rel_tol=1.0,
+        precision_weighted_smooth=False,
+    )
+    assert out.x == (15.0, 20.0)
diff --git a/test/test_clustering_quality_checkpoint.py b/test/test_clustering_quality_checkpoint.py
new file mode 100644
index 0000000..bdce2f7
--- /dev/null
+++ b/test/test_clustering_quality_checkpoint.py
@@ -0,0 +1,137 @@
+"""Tests for clustering quality checkpoint and reporting round-trip helpers."""
+
+from __future__ import annotations
+
+import json
+import pathlib
+
+import pytest
+
+from pelinker.clustering_quality_checkpoint import (
+    CHECKPOINT_VERSION,
+    ClusteringQualityCheckpoint,
+    combination_key_from_members,
+    compute_run_fingerprint,
+    fingerprint_config_from_cli,
+    load_checkpoint,
+    new_checkpoint,
+    reconcile_fusion_checkpoint_params,
+    save_checkpoint_atomic,
+)
+from pelinker.reporting import (
+    ClusteringSearchSummaryRow,
+    HyperparameterSearchStats,
+    MeanWithUncertainty,
+    clustering_search_summary_row_from_flat_dict,
+)
+
+
+def test_combination_key_sorted_stable() -> None:
+    a = combination_key_from_members([("b", "L2"), ("a", "L1")])
+    b = combination_key_from_members([("a", "L1"), ("b", "L2")])
+    assert a == b == "2:a/L1+b/L2"
+    assert combination_key_from_members([("m", "l")]) == "1:m/l"
+
+
+def test_checkpoint_save_load_roundtrip(tmp_path: pathlib.Path) -> None:
+    fp = compute_run_fingerprint({"a": 1, "b": 2})
+    ckpt = new_checkpoint(fp)
+    ckpt.completed_combinations.append("1:m/l")
+    ckpt.summaries_by_key["1:m/l"] = {"model": "m", "layer": "l", "best_score": 0.9}
+    path = tmp_path / "st.json"
+    save_checkpoint_atomic(path, ckpt)
+    loaded = load_checkpoint(path)
+    assert loaded.run_fingerprint == fp
+    assert loaded.completed_combinations == ["1:m/l"]
+    assert loaded.summaries_by_key["1:m/l"]["best_score"] == 0.9
+
+
+def test_load_wrong_version_raises(tmp_path: pathlib.Path) -> None:
+    path = tmp_path / "bad.json"
+    path.write_text(
+        json.dumps({"version": 999, "run_fingerprint": "x"}), encoding="utf-8"
+    )
+    with pytest.raises(ValueError, match="Unsupported checkpoint version"):
+        load_checkpoint(path)
+
+
+def test_fingerprint_differs_for_different_config() -> None:
+    a = compute_run_fingerprint({"seed": 1})
+    b = compute_run_fingerprint({"seed": 2})
+    assert a != b
+
+
+def test_reconcile_drops_fusion_when_fusion_params_change(
+    tmp_path: pathlib.Path,
+) -> None:
+    fp = compute_run_fingerprint({"k": 1})
+    ckpt = new_checkpoint(fp)
+    ckpt.checkpoint_fusion_pairs = 0
+    ckpt.checkpoint_fusion_triples = 0
+    ckpt.completed_combinations = ["1:a/l", "2:a/L1+b/L2"]
+    ckpt.summaries_by_key = {
+        "1:a/l": {"model": "a", "layer": "l"},
+        "2:a/L1+b/L2": {"model": "fusion2", "layer": "a/L1+b/L2"},
+    }
+    n = reconcile_fusion_checkpoint_params(ckpt, fusion_pairs=5, fusion_triples=0)
+    assert n == 1
+    assert ckpt.completed_combinations == ["1:a/l"]
+    assert list(ckpt.summaries_by_key.keys()) == ["1:a/l"]
+    assert ckpt.checkpoint_fusion_pairs == 5
+
+
+def test_fingerprint_depends_on_input_dir_not_fusion_cli(
+    tmp_path: pathlib.Path,
+) -> None:
+    a_dir = tmp_path / "a"
+    b_dir = tmp_path / "b"
+    a_dir.mkdir()
+    b_dir.mkdir()
+    common = dict(
+        umap_dim=8,
+        pca_components=100,
+        min_class_size=20,
+        seed=1,
+        frac=0.1,
+        n_embedding_batches=None,
+        batch_size=1000,
+        prefix="p",
+        n_sample=1,
+        selected_labels_kb_path=None,
+        max_scale=60,
+    )
+    fa = compute_run_fingerprint(fingerprint_config_from_cli(input_dir=a_dir, **common))
+    fb = compute_run_fingerprint(fingerprint_config_from_cli(input_dir=b_dir, **common))
+    assert fa != fb
+
+
+def test_summary_flat_dict_round_trip() -> None:
+    row = ClusteringSearchSummaryRow(
+        model="m",
+        layer="l",
+        hyperparameters=HyperparameterSearchStats(
+            min_cluster_size=MeanWithUncertainty(mean=10.0, std=1.0),
+        ),
+        number_properties=MeanWithUncertainty(mean=5.0, std=0.0),
+        n_clusters_emergent=MeanWithUncertainty(mean=3.0, std=0.5),
+        dbcv=MeanWithUncertainty(mean=0.44, std=0.01),
+        ari=MeanWithUncertainty(mean=0.8, std=0.05),
+    )
+    flat = row.to_flat_dict()
+    back = clustering_search_summary_row_from_flat_dict(flat)
+    assert back.model == row.model
+    assert back.layer == row.layer
+    assert back.dbcv.mean == row.dbcv.mean
+    assert back.ari is not None
+    assert back.ari.mean == 0.8
+
+
+def test_checkpoint_to_json_dict_sorted_keys(tmp_path: pathlib.Path) -> None:
+    ckpt = ClusteringQualityCheckpoint(
+        version=CHECKPOINT_VERSION,
+        run_fingerprint="ab",
+        summaries_by_key={"z": {"model": "z"}, "a": {"model": "a"}},
+    )
+    d = ckpt.to_json_dict()
+    keys = list(d["summaries_by_key"].keys())
+    assert keys == ["a", "z"]
diff --git a/test/test_clustering_quality_fine_metadata.py b/test/test_clustering_quality_fine_metadata.py
new file mode 100644
index 0000000..a3eefcf
--- /dev/null
+++ b/test/test_clustering_quality_fine_metadata.py
@@ -0,0 +1,64 @@
+"""Tests for fine clustering metadata extraction."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from pelinker.reporting import ClusteringHyperparameters, ClusteringReport
+from run.analysis.clustering_quality import _fine_clustering_metadata_df
+
+
+def _report_with_df(df: pd.DataFrame) -> ClusteringReport:
+    return ClusteringReport(
+        hyperparameters=ClusteringHyperparameters(min_cluster_size=10),
+        best_score=0.5,
+        number_properties=2,
+        n_clusters_emergent=2,
+        metrics_df=pd.DataFrame({"min_cluster_size": [10], "dbcv": [0.5]}),
+        df=df,
+        ari=0.1,
+    )
+
+
+def test_fine_metadata_includes_core_and_optional_columns() -> None:
+    report = _report_with_df(
+        pd.DataFrame(
+            {
+                "pmid": [1],
+                "mention": ["abc"],
+                "property": ["term_a"],
+                "class": [3],
+            }
+        )
+    )
+    got = _fine_clustering_metadata_df(
+        report,
+        model="m",
+        layer="L1",
+        sample_idx=0,
+    )
+    assert list(got.columns) == [
+        "model",
+        "layer",
+        "sample_idx",
+        "property",
+        "class",
+        "pmid",
+        "mention",
+    ]
+    assert got.iloc[0]["model"] == "m"
+    assert got.iloc[0]["layer"] == "L1"
+    assert got.iloc[0]["sample_idx"] == 0
+    assert got.iloc[0]["property"] == "term_a"
+    assert got.iloc[0]["class"] == 3
+
+
+def test_fine_metadata_returns_empty_if_required_columns_missing() -> None:
+    report = _report_with_df(pd.DataFrame({"property": ["term_a"]}))
+    got = _fine_clustering_metadata_df(
+        report,
+        model="m",
+        layer="L1",
+        sample_idx=1,
+    )
+    assert got.empty
diff --git a/test/test_config.py b/test/test_config.py
new file mode 100644
index 0000000..fe5ce87
--- /dev/null
+++ b/test/test_config.py
@@ -0,0 +1,53 @@
+from datetime import date
+
+import pytest
+
+from pelinker.config import KBConfig
+
+
+def test_kb_config_ok():
+    c = KBConfig(
+        name="test-kb",
+        version="1.2.3",
+        created_at=date(2025, 1, 15),
+        description="demo",
+        entity_count=42,
+    )
+    assert c.entity_count == 42
+
+
+def test_kb_config_entity_count_optional():
+    c = KBConfig(name="x", version="0.0.1", created_at=date.today())
+    assert c.entity_count is None
+
+
+def test_kb_config_semver_prerelease_build():
+    KBConfig(
+        name="x",
+        version="1.0.0-rc.1+build.9",
+        created_at=date.today(),
+    )
+
+
+@pytest.mark.parametrize(
+    "version",
+    ["", "1", "1.0", "v1.0.0", "01.0.0", "1.0.a"],
+)
+def test_kb_config_rejects_bad_semver(version: str):
+    with pytest.raises(ValueError):
+        KBConfig(name="x", version=version, created_at=date.today())
+
+
+def test_kb_config_rejects_empty_name():
+    with pytest.raises(ValueError):
+        KBConfig(name="   ", version="1.0.0", created_at=date.today())
+
+
+def test_kb_config_rejects_negative_entity_count():
+    with pytest.raises(ValueError):
+        KBConfig(
+            name="x",
+            version="1.0.0",
+            created_at=date.today(),
+            entity_count=-1,
+        )
diff --git a/test/test_embedding_fusion.py b/test/test_embedding_fusion.py
new file mode 100644
index 0000000..febdc92
--- /dev/null
+++ b/test/test_embedding_fusion.py
@@ -0,0 +1,134 @@
+import numpy as np
+import pandas as pd
+
+from pelinker.embedding_fusion import (
+    JOIN_KEYS,
+    dedupe_mean_embed_by_keys,
+    fused_property_vectors_from_paths,
+    mention_level_concat_frames,
+)
+
+
+def test_mention_level_concat_inner_join():
+    df1 = pd.DataFrame(
+        {
+            "pmid": ["1", "1"],
+            "property": ["a", "b"],
+            "mention": ["x", "y"],
+            "embed": [[1.0, 0.0], [0.0, 1.0]],
+        }
+    )
+    df2 = pd.DataFrame(
+        {
+            "pmid": ["1", "1"],
+            "property": ["a", "b"],
+            "mention": ["x", "z"],
+            "embed": [[2.0, 2.0], [3.0, 3.0]],
+        }
+    )
+    out = mention_level_concat_frames([df1, df2])
+    assert len(out) == 1
+    row = out.iloc[0]
+    assert row["property"] == "a"
+    assert row["mention"] == "x"
+    np.testing.assert_array_equal(
+        row["embed"], np.array([1.0, 0.0, 2.0, 2.0], dtype=np.float32)
+    )
+
+
+def test_mention_level_dupes_averaged_within_source():
+    df1 = pd.DataFrame(
+        {
+            "pmid": ["1", "1"],
+            "property": ["a", "a"],
+            "mention": ["x", "x"],
+            "embed": [[0.0, 2.0], [2.0, 0.0]],
+        }
+    )
+    df2 = pd.DataFrame(
+        {
+            "pmid": ["1"],
+            "property": ["a"],
+            "mention": ["x"],
+            "embed": [[1.0, 1.0]],
+        }
+    )
+    out = mention_level_concat_frames([df1, df2])
+    assert len(out) == 1
+    np.testing.assert_allclose(out.iloc[0]["embed"], [1.0, 1.0, 1.0, 1.0], rtol=1e-5)
+
+
+def test_mention_level_single_frame_rename():
+    df = pd.DataFrame(
+        {
+            "pmid": ["1"],
+            "property": ["p"],
+            "mention": ["m"],
+            "embed": [[1.0, 2.0]],
+        }
+    )
+    out = mention_level_concat_frames([df])
+    assert list(out.columns) == list(JOIN_KEYS) + ["embed"]
+    np.testing.assert_array_equal(out.iloc[0]["embed"], np.array([1.0, 2.0]))
+
+
+def test_dedupe_mean_embed():
+    df = pd.DataFrame(
+        {
+            "pmid": ["1", "1"],
+            "property": ["a", "a"],
+            "mention": ["z", "z"],
+            "embed": [[1.0, 0.0], [0.0, 3.0]],
+        }
+    )
+    out = dedupe_mean_embed_by_keys(df, keys=JOIN_KEYS)
+    assert len(out) == 1
+    np.testing.assert_array_equal(out.iloc[0]["embed"], np.array([0.5, 1.5]))
+
+
+def test_property_fused_vectors_from_parquet_paths(tmp_path):
+    p1 = tmp_path / "a.parquet"
+    p2 = tmp_path / "b.parquet"
+    pd.DataFrame(
+        {
+            "pmid": ["1"],
+            "property": ["foo"],
+            "mention": ["m"],
+            "embed": [[1.0, 0.0]],
+        }
+    ).to_parquet(p1)
+    pd.DataFrame(
+        {
+            "pmid": ["1"],
+            "property": ["foo"],
+            "mention": ["m"],
+            "embed": [[0.0, 2.0]],
+        }
+    ).to_parquet(p2)
+    fused = fused_property_vectors_from_paths([p1, p2], kb_labels=None)
+    assert "foo" in fused
+    np.testing.assert_array_equal(fused["foo"], np.array([1.0, 0.0, 0.0, 2.0]))
+
+
+def test_property_fused_intersection(tmp_path):
+    p1 = tmp_path / "a.parquet"
+    p2 = tmp_path / "b.parquet"
+    pd.DataFrame(
+        {
+            "pmid": ["1", "1"],
+            "property": ["only_a", "both"],
+            "mention": ["m", "m"],
+            "embed": [[1.0], [3.0]],
+        }
+    ).to_parquet(p1)
+    pd.DataFrame(
+        {
+            "pmid": ["1"],
+            "property": ["both"],
+            "mention": ["m"],
+            "embed": [[4.0]],
+        }
+    ).to_parquet(p2)
+    fused = fused_property_vectors_from_paths([p1, p2], kb_labels=None)
+    assert set(fused.keys()) == {"both"}
+    np.testing.assert_array_equal(fused["both"], np.array([3.0, 4.0]))
diff --git a/test/test_fit_embedding_metadata.py b/test/test_fit_embedding_metadata.py
new file mode 100644
index 0000000..0df1fc5
--- /dev/null
+++ b/test/test_fit_embedding_metadata.py
@@ -0,0 +1,109 @@
+"""Tests for fit CLI embedding metadata (filename inference and per-path sources)."""
+
+from pathlib import Path
+
+import pytest
+
+from pelinker.cli.fit import (
+    _embedding_metadata,
+    _parse_embedding_parquet_path,
+    _parse_embedding_parquet_stem,
+)
+
+
+@pytest.mark.parametrize(
+    ("stem", "expected"),
+    [
+        ("res_pubmedbert_1", ("pubmedbert", "1")),
+        ("corpus_pubmedbert_12", ("pubmedbert", "12")),
+        ("out_res_biobert_1_2_3", ("biobert", "1,2,3")),
+        ("pubmedbert_1", ("pubmedbert", "1")),
+        ("prefix_bluebert_9", ("bluebert", "9")),
+        ("x_scibert_2", ("scibert", "2")),
+        ("run_bert_1", ("bert", "1")),
+        ("res_biobert-stsb_1", ("biobert-stsb", "1")),
+    ],
+)
+def test_parse_embedding_parquet_stem_ok(stem: str, expected: tuple[str, str]) -> None:
+    assert _parse_embedding_parquet_stem(stem) == expected
+
+
+@pytest.mark.parametrize(
+    "stem",
+    [
+        "mentions",
+        "corpus_smoke_trunc",
+        "unknownmodel_1",
+        "res_pubmedbert_",
+        "res_pubmedbert_xy",
+    ],
+)
+def test_parse_embedding_parquet_stem_none(stem: str) -> None:
+    assert _parse_embedding_parquet_stem(stem) is None
+
+
+def test_parse_embedding_parquet_path_parquet_gz(tmp_path: Path) -> None:
+    p = tmp_path / "res_pubmedbert_1.parquet.gz"
+    p.touch()
+    assert _parse_embedding_parquet_path(p) == ("pubmedbert", "1")
+
+
+def test_embedding_metadata_infers_per_path_when_lists_omitted(tmp_path: Path) -> None:
+    a = tmp_path / "res_pubmedbert_1.parquet"
+    b = tmp_path / "res_biobert_2.parquet"
+    a.touch()
+    b.touch()
+    meta = _embedding_metadata(
+        [a, b],
+        model_type="pubmedbert",
+        layers_spec="1",
+        model_types=None,
+        layers_specs=None,
+    )
+    assert [s.model_type for s in meta.sources] == ["pubmedbert", "biobert"]
+    assert [s.layers_spec for s in meta.sources] == ["1", "2"]
+
+
+def test_embedding_metadata_explicit_lists_override_filename(tmp_path: Path) -> None:
+    a = tmp_path / "res_pubmedbert_1.parquet"
+    a.touch()
+    meta = _embedding_metadata(
+        [a],
+        model_type="bert",
+        layers_spec="9",
+        model_types=["scibert"],
+        layers_specs=["3"],
+    )
+    assert meta.sources[0].model_type == "scibert"
+    assert meta.sources[0].layers_spec == "3"
+
+
+def test_embedding_metadata_mixed_infer_layers_only(tmp_path: Path) -> None:
+    a = tmp_path / "res_pubmedbert_1.parquet"
+    b = tmp_path / "res_pubmedbert_2.parquet"
+    a.touch()
+    b.touch()
+    meta = _embedding_metadata(
+        [a, b],
+        model_type="pubmedbert",
+        layers_spec="1",
+        model_types=["pubmedbert", "pubmedbert"],
+        layers_specs=None,
+    )
+    assert [s.layers_spec for s in meta.sources] == ["1", "2"]
+
+
+def test_embedding_metadata_mixed_infer_model_only(tmp_path: Path) -> None:
+    a = tmp_path / "res_pubmedbert_1.parquet"
+    b = tmp_path / "res_biobert_1.parquet"
+    a.touch()
+    b.touch()
+    meta = _embedding_metadata(
+        [a, b],
+        model_type="bert",
+        layers_spec="1",
+        model_types=None,
+        layers_specs=["1", "1"],
+    )
+    assert [s.model_type for s in meta.sources] == ["pubmedbert", "biobert"]
+    assert [s.layers_spec for s in meta.sources] == ["1", "1"]
diff --git a/test/test_linker_fused_embeddings.py b/test/test_linker_fused_embeddings.py
new file mode 100644
index 0000000..90b831b
--- /dev/null
+++ b/test/test_linker_fused_embeddings.py
@@ -0,0 +1,168 @@
+"""Mention-level fused parquets for ``Linker.fit`` (inner join + concat, analysis-aligned)."""
+
+import pandas as pd
+
+from numpy.random import RandomState
+
+from pelinker.config import (
+    ClusteringOptimizationConfig,
+    EmbeddingModelMetadata,
+    EmbeddingSourceSpec,
+)
+from pelinker.model import Linker
+from pelinker.transform import TransformConfig
+
+
+def test_fused_fit_two_parquets_stacks_embedding_dim(tmp_path):
+    metadata = EmbeddingModelMetadata(
+        sources=(
+            EmbeddingSourceSpec(model_type="a", layers_spec="1"),
+            EmbeddingSourceSpec(model_type="a", layers_spec="2"),
+        )
+    )
+    n_ent = 24
+    labels_map = {f"e{k}": f"p{k}" for k in range(n_ent)}
+
+    p1 = tmp_path / "s0.parquet"
+    p2 = tmp_path / "s1.parquet"
+    rows1 = []
+    rows2 = []
+    for k in range(n_ent):
+        rows1.append(
+            {"pmid": "1", "property": f"p{k}", "mention": "m", "embed": [float(k), 1.0]}
+        )
+        rows2.append(
+            {
+                "pmid": "1",
+                "property": f"p{k}",
+                "mention": "m",
+                "embed": [0.5, float(k) * 0.1],
+            }
+        )
+    pd.DataFrame(rows1).to_parquet(p1)
+    pd.DataFrame(rows2).to_parquet(p2)
+
+    opt = ClusteringOptimizationConfig(
+        min_class_size=1,
+        max_scale=30,
+        batch_size=500,
+    )
+    linker = Linker(labels_map=labels_map, embedding_metadata=metadata)
+    linker.fit(
+        [p1, p2],
+        transform_config=TransformConfig(
+            pca_components=4, umap_components=2, umap_viz_components=2
+        ),
+        min_cluster_size=2,
+        clustering_optimization_config=opt,
+    )
+    assert linker.transformer is not None
+    assert len(linker.vocabulary) == n_ent
+    assert linker.transformer.pca is not None
+    assert linker.transformer.pca.n_features_in_ == 4
+    assert linker.clusterer is not None
+    assert getattr(linker.clusterer, "prediction_data_", None) is not None
+    assert linker.training_cluster_frame is not None
+    assert len(linker.training_cluster_frame) == n_ent
+    assert set(linker.training_cluster_frame.columns) >= {
+        "pmid",
+        "property",
+        "mention",
+        "cluster",
+    }
+
+
+def test_fused_fit_optimize_clustering_grid_then_full_fit(tmp_path):
+    """Grid search uses ``frac`` subsample; final model fits on all prepared rows."""
+    metadata = EmbeddingModelMetadata(
+        sources=(
+            EmbeddingSourceSpec(model_type="a", layers_spec="1"),
+            EmbeddingSourceSpec(model_type="a", layers_spec="2"),
+        )
+    )
+    n_ent = 20
+    labels_map = {f"e{k}": f"p{k}" for k in range(n_ent)}
+    p1 = tmp_path / "s0.parquet"
+    p2 = tmp_path / "s1.parquet"
+    rows1 = []
+    rows2 = []
+    for k in range(n_ent):
+        for pmid in ("a", "b", "c", "d"):
+            rows1.append(
+                {
+                    "pmid": pmid,
+                    "property": f"p{k}",
+                    "mention": "m",
+                    "embed": [float(k), 0.1],
+                }
+            )
+            rows2.append(
+                {
+                    "pmid": pmid,
+                    "property": f"p{k}",
+                    "mention": "m",
+                    "embed": [0.2, float(k) * 0.05],
+                }
+            )
+    pd.DataFrame(rows1).to_parquet(p1)
+    pd.DataFrame(rows2).to_parquet(p2)
+
+    opt = ClusteringOptimizationConfig(
+        min_class_size=4,
+        max_scale=25,
+        frac=0.7,
+        rns=RandomState(7),
+        batch_size=500,
+    )
+    linker = Linker(labels_map=labels_map, embedding_metadata=metadata)
+    linker.fit(
+        [p1, p2],
+        transform_config=TransformConfig(
+            pca_components=4, umap_components=2, umap_viz_components=2
+        ),
+        optimize_clustering=True,
+        clustering_optimization_config=opt,
+    )
+    assert linker.clusterer is not None
+    assert linker.training_cluster_frame is not None
+    assert len(linker.training_cluster_frame) == 4 * n_ent
+
+
+def test_estimate_model_clustering_multi_file_paths(tmp_path):
+    from pelinker.analysis import estimate_model_clustering
+
+    p1 = tmp_path / "s0.parquet"
+    p2 = tmp_path / "s1.parquet"
+    n = 25
+    pd.DataFrame(
+        {
+            "pmid": [str(i) for i in range(n)],
+            "property": ["foo"] * n,
+            "mention": ["m"] * n,
+            "embed": [[float(i), 0.0] for i in range(n)],
+        }
+    ).to_parquet(p1)
+    pd.DataFrame(
+        {
+            "pmid": [str(i) for i in range(n)],
+            "property": ["foo"] * n,
+            "mention": ["m"] * n,
+            "embed": [[0.0, float(i)] for i in range(n)],
+        }
+    ).to_parquet(p2)
+
+    report = estimate_model_clustering(
+        transform_config=TransformConfig(
+            pca_components=4, umap_components=2, umap_viz_components=2
+        ),
+        file_paths=[p1, p2],
+        optimization_config=ClusteringOptimizationConfig(
+            min_class_size=20,
+            max_scale=40,
+            rns=RandomState(0),
+            frac=1.0,
+        ),
+    )
+    assert report is not None
+    assert report.df is not None
+    assert "u_00" in report.df.columns
diff --git a/test/test_model.py b/test/test_model.py
index 0caac4b..c1d4dfc 100644
--- a/test/test_model.py
+++ b/test/test_model.py
@@ -17,7 +17,7 @@ def text():
 
 
 @pytest.mark.skip("fix later")
-def test_load(text, tokenizer_model_biobert_stsb, nlp):
+def test_load(text, nlp):
     layer_spec = "sent"
     layers = Linker.str2layers(layer_spec)
 
@@ -27,7 +27,6 @@ def test_load(text, tokenizer_model_biobert_stsb, nlp):
         f"pelinker.model.biobert-stsb.{layers_str}"
     )
     model = Linker.load(file_path)
-    t_tokenizer, t_model = tokenizer_model_biobert_stsb
 
-    r = model.predict([text], t_tokenizer, t_model, nlp, MAX_LENGTH, True)
+    r = model.predict([text], nlp, MAX_LENGTH, threshold=1.0)
     assert r["entities"][-1]["mention"] == "decreased"
diff --git a/test/test_plot_dbcv_ari_scatter.py b/test/test_plot_dbcv_ari_scatter.py
new file mode 100644
index 0000000..f90f00d
--- /dev/null
+++ b/test/test_plot_dbcv_ari_scatter.py
@@ -0,0 +1,89 @@
+"""Smoke tests for DBCV vs ARI grid scatter."""
+
+from __future__ import annotations
+
+import pathlib
+
+import pandas as pd
+
+from pelinker.plotting import (
+    GRID_COL_SAMPLE_BEST_DBCV,
+    GRID_COL_SAMPLE_ARI,
+    _layer_spec_code,
+    plot_dbcv_vs_ari_from_grid,
+)
+
+
+def test_layer_spec_code() -> None:
+    assert _layer_spec_code("m1", "2") == "2"
+    assert _layer_spec_code("fusion2", "bluebert/2+pubmedbert/3") == "2,3"
+    assert _layer_spec_code("fusion3", "a/2+b/2+c/3") == "2,2,3"
+
+
+def test_plot_dbcv_vs_ari_from_grid_writes_file(tmp_path: pathlib.Path) -> None:
+    rows = []
+    for sample_idx in (0, 1):
+        for mcs in (10, 20):
+            rows.append(
+                {
+                    "model": "m1",
+                    "layer": "2",
+                    "sample_idx": sample_idx,
+                    "min_cluster_size": mcs,
+                    "icm": 0.1,
+                    "n_clusters": 3,
+                    "dbcv": 0.2 + mcs * 0.01 + sample_idx * 0.05,
+                    GRID_COL_SAMPLE_BEST_DBCV: 0.5 + sample_idx * 0.02,
+                    GRID_COL_SAMPLE_ARI: 0.7 + sample_idx * 0.03,
+                }
+            )
+    df = pd.DataFrame(rows)
+    out = tmp_path / "s.png"
+    assert plot_dbcv_vs_ari_from_grid(df, out) is True
+    assert out.is_file()
+
+
+def test_plot_dbcv_vs_ari_fusion_multicolor(tmp_path: pathlib.Path) -> None:
+    """Pair / triple fusion rows exercise custom square and wedge markers."""
+    rows: list[dict[str, object]] = []
+    for sample_idx in (0, 1):
+        rows.append(
+            {
+                "model": "fusion2",
+                "layer": "bluebert/2+pubmedbert/3",
+                "sample_idx": sample_idx,
+                "min_cluster_size": 20,
+                "dbcv": 0.4,
+                GRID_COL_SAMPLE_BEST_DBCV: 0.5 + sample_idx * 0.01,
+                GRID_COL_SAMPLE_ARI: 0.75 + sample_idx * 0.02,
+            }
+        )
+        rows.append(
+            {
+                "model": "fusion3",
+                "layer": "bluebert/2+pubmedbert/2+pubmedbert/3",
+                "sample_idx": sample_idx,
+                "min_cluster_size": 20,
+                "dbcv": 0.4,
+                GRID_COL_SAMPLE_BEST_DBCV: 0.55 + sample_idx * 0.01,
+                GRID_COL_SAMPLE_ARI: 0.72 + sample_idx * 0.02,
+            }
+        )
+    df = pd.DataFrame(rows)
+    out = tmp_path / "fusion.png"
+    assert plot_dbcv_vs_ari_from_grid(df, out) is True
+    assert out.is_file()
+
+
+def test_plot_dbcv_vs_ari_missing_columns_skips(tmp_path: pathlib.Path) -> None:
+    df = pd.DataFrame(
+        {
+            "model": ["a"],
+            "layer": ["1"],
+            "sample_idx": [0],
+            "dbcv": [0.1],
+        }
+    )
+    out = tmp_path / "x.png"
+    assert plot_dbcv_vs_ari_from_grid(df, out) is False
+    assert not out.exists()
diff --git a/test/test_reporting_clustering.py b/test/test_reporting_clustering.py
new file mode 100644
index 0000000..0eb3293
--- /dev/null
+++ b/test/test_reporting_clustering.py
@@ -0,0 +1,75 @@
+"""Tests for typed clustering reports and multi-sample summaries."""
+
+import pandas as pd
+import pytest
+
+from pelinker.reporting import (
+    ClusteringHyperparameters,
+    ClusteringReport,
+    ClusteringSearchSummaryRow,
+    summarize_clustering_reports_for_search,
+)
+
+
+def _minimal_report(
+    min_cluster_size: int,
+    best_score: float,
+    *,
+    n_clusters_emergent: int = 3,
+    ari: float | None = None,
+) -> ClusteringReport:
+    metrics_df = pd.DataFrame(
+        {"min_cluster_size": [min_cluster_size], "dbcv": [best_score]}
+    )
+    df = pd.DataFrame({"x": [1.0]})
+    return ClusteringReport(
+        hyperparameters=ClusteringHyperparameters(min_cluster_size=min_cluster_size),
+        best_score=best_score,
+        number_properties=5,
+        n_clusters_emergent=n_clusters_emergent,
+        metrics_df=metrics_df,
+        df=df,
+        ari=ari,
+    )
+
+
+def test_clustering_report_best_size_alias() -> None:
+    r = _minimal_report(42, 0.88)
+    assert r.best_size == 42
+    assert r.hyperparameters.min_cluster_size == 42
+
+
+def test_summarize_clustering_reports_for_search_means_and_flat_dict() -> None:
+    r1 = _minimal_report(10, 0.5, n_clusters_emergent=4, ari=0.8)
+    r2 = _minimal_report(20, 0.7, n_clusters_emergent=8, ari=0.9)
+    row = summarize_clustering_reports_for_search(
+        [r1, r2],
+        model="m1",
+        layer="L0",
+    )
+    assert isinstance(row, ClusteringSearchSummaryRow)
+    assert row.hyperparameters.min_cluster_size.mean == pytest.approx(15.0)
+    assert row.hyperparameters.min_cluster_size.std > 0
+    assert row.dbcv.mean == pytest.approx(0.6)
+    flat = row.to_flat_dict()
+    assert flat["model"] == "m1"
+    assert flat["layer"] == "L0"
+    assert flat["best_size"] == pytest.approx(15.0)
+    assert flat["best_score"] == pytest.approx(0.6)
+    assert flat["n_clusters_emergent"] == pytest.approx(6.0)
+    assert flat["n_clusters_emergent_std"] > 0
+    assert flat["ari"] == pytest.approx(0.85)
+
+
+def test_summarize_empty_raises() -> None:
+    with pytest.raises(ValueError, match="non-empty"):
+        summarize_clustering_reports_for_search([], model="m", layer="l")
+
+
+def test_summarize_ari_none_when_all_missing() -> None:
+    r = _minimal_report(5, 0.3, ari=None)
+    row = summarize_clustering_reports_for_search([r], model="m", layer="l")
+    assert row.ari is None
+    flat = row.to_flat_dict()
+    assert flat["ari"] is None
+    assert flat["ari_std"] == 0.0
diff --git a/test/test_util.py b/test/test_util.py
index f11a157..246443d 100644
--- a/test/test_util.py
+++ b/test/test_util.py
@@ -4,7 +4,6 @@
 from pelinker.util import (
     get_word_boundaries,
     map_spans_to_spans_basic,
-    get_vb_spans,
     text_to_tokens,
     map_words_to_tokens,
     split_text_into_batches,
@@ -13,11 +12,6 @@
 )
 
 
-def test_vb_span(nlp, phrase_vb_0):
-    spans0 = get_vb_spans(nlp, text=phrase_vb_0, extra_context=True)
-    assert len(spans0) == 3
-
-
 def test_text_info(nlp, phrase_vb_0):
     tokens = text_to_tokens(nlp, text=phrase_vb_0)
     assert isinstance(tokens[0], SimplifiedToken)
diff --git a/uv.lock b/uv.lock
index 3ec0422..b87ed2f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,3758 +1,3768 @@
-version = 1
-revision = 3
 requires-python = ">=3.10.6"
 resolution-markers = [
-    "python_full_version >= '3.13'",
-    "python_full_version == '3.12.*'",
-    "python_full_version == '3.11.*'",
-    "python_full_version < '3.11'",
+  "python_full_version < '3.11'",
+  "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+  "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform == 'emscripten'",
+  "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform == 'win32'",
+  "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+  "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+  "python_full_version >= '3.14' and sys_platform == 'win32'"
 ]
+revision = 3
+version = 1
 
 [[package]]
-name = "aniso8601"
-version = "10.0.1"
-source = { registry = "https://pypi.org/simple" }
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