Release 0.7.13 v2 routing assets and quality priors

Author: anjieyang

.gitignore

@@ -44,6 +44,9 @@ spending.json
 bench/results/
 bench/data/*.generated.jsonl
 
+# Local experiment notes
+ROUTING_EXPERIMENT_REPORT.md
+
 # ─── v2: internal design docs (not user-facing) ───
 docs/uncommon-route-v2-architecture.md
 docs/telemetry-design.md

openclaw-plugin/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@anjieyang/uncommon-route",
-  "version": "0.7.12",
+  "version": "0.7.13",
   "description": "OpenClaw plugin for UncommonRoute, the local LLM router that cuts premium-model spend",
   "type": "module",
   "main": "src/index.js",

openclaw-plugin/src/index.js

@@ -16,7 +16,7 @@
 import { spawn, execSync } from "node:child_process";
 import { setTimeout as sleep } from "node:timers/promises";
 
-const VERSION = "0.7.12";
+const VERSION = "0.7.13";
 const DEFAULT_PORT = 8403;
 const DEFAULT_UPSTREAM = "";
 const HEALTH_TIMEOUT_MS = 15_000;

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "uncommon-route"
-version = "0.7.12"
+version = "0.7.13"
 description = "Local LLM router that cuts premium-model spend with adaptive 3-tier routing, OpenAI + Anthropic compatible"
 requires-python = ">=3.11"
 license = "MIT"
@@ -30,6 +30,7 @@ dependencies = [
 [project.optional-dependencies]
 v2 = [
     "sentence-transformers>=2.2",
+    "xgboost>=2.0",
 ]
 dev = [
     "build>=1.2",
@@ -55,7 +56,16 @@ build-backend = "setuptools.build_meta"
 include = ["uncommon_route*"]
 
 [tool.setuptools.package-data]
-uncommon_route = ["router/model.json", "router/benchmark_seed.json", "static/**/*"]
+uncommon_route = [
+    "router/model.json",
+    "router/benchmark_seed.json",
+    "static/**/*",
+    "data/v2_splits/seed_embeddings.npy",
+    "data/v2_splits/seed_labels.json",
+    "data/v2_splits/embedding_classifier.pkl",
+    "data/v2_splits/meta_scaler.pkl",
+    "data/v2_splits/calibration_params.json",
+]
 
 [tool.pytest.ini_options]
 testpaths = ["tests"]

tests/test_ensemble.py

@@ -47,6 +47,15 @@ def test_result_has_method():
     assert result.method in ("direct", "conservative")
 
 
+def test_single_low_confidence_vote_does_not_become_certain():
+    votes = [TierVote(tier_id=3, confidence=0.30)]
+    ens = Ensemble(weights=[1.0])
+    result = ens.decide(votes)
+    assert result.confidence == 0.30
+    assert result.raw_confidence == 0.30
+    assert result.method == "conservative"
+
+
 def test_conservative_fallback_caps_at_tier_3():
     """When best_tier=3 but confidence is low, conservative path should cap at 3 (not 4)."""
     # tier 3 wins the vote but with only ~52% confidence → below conservative threshold