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docs/changelog.md

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 # Changelog
 
-All notable changes to the Alignment Framework.
-
-## [0.2.0] - 2025-10-03
-
-### Major Release: Information-Theoretic Metrics & Service Layer
-
-This release introduces **intelligent, redundancy-aware pruning** with a complete service layer architecture and three new information-theoretic metrics.
-
----
+## Recent Updates
 
 ### Added
-
-#### New Service Layer
-- `alignment.services.ActivationCaptureService` - Centralized activation and weight collection
-- `alignment.services.NodeScoringService` - Composite importance scoring engine
-- `alignment.services.MaskOperations` - Unified pruning mask utilities
-- `alignment.services.ActivationData` - Dataclass for captured data
-- `alignment.services.CompositeScores` - Dataclass for multi-metric scores
-
-**Impact:** Reduces code duplication by ~40%, cleaner APIs
-
-#### New Information-Theoretic Metrics
-- `alignment.metrics.PairwiseRedundancyGaussian` - Per-neuron redundancy measurement
-  - Computes `R(Y_i, Y_j) = -0.5 * log(1 - ρ²)` for sampled neuron pairs
-  - Identifies redundant neurons for pruning
-  - Supports random, nearest, and all-pairs sampling
-
-- `alignment.metrics.SynergyGaussianMMI` - Target-conditional synergy measurement
-  - Computes `S_MMI(Z; Y_i, Y_j)` using MMI redundancy axiom
-  - Identifies complementary neuron pairs
-  - Preserves synergistic features during pruning
-
-**Impact:** Enables redundancy-aware pruning with expected +3-5% accuracy retention
-
-#### Enhanced Existing Metrics
-- `alignment.metrics.RayleighQuotient.compute_class_conditioned()` - New method
-  - Class-conditioned RQ: `RQ(w; Σ_{X|y})`
-  - Discriminative alignment: `ΔRQ = RQ - E[RQ|class]`
-  - Identifies task-relevant neurons
-
-**Impact:** Task-aware pruning decisions
-
-#### **Hook Management** 🔒
-- `alignment.models.HookManager` - Context-managed hook lifecycle
-- `alignment.models.PersistentHookManager` - Long-lived hooks with auto-cleanup
-- `BaseModelWrapper.forward_with_activations()` - Safe activation capture
-- `BaseModelWrapper.capture_activations_safe()` - Convenience method
-
-**Impact:** Prevents memory leaks in long experiments
-
-#### **Examples** 📖
-- `examples/06_redundancy_aware_pruning.py` - Complete workflow demonstration
-  - Shows all new features in action
-  - Compares redundancy-aware vs baseline pruning
-  - Demonstrates ΔRQ analysis
-
----
+- Service layer architecture: `ActivationCaptureService`, `NodeScoringService`, `MaskOperations`
+- Information-theoretic metrics: `PairwiseRedundancyGaussian`, `SynergyGaussianMMI`
+- Class-conditioned RQ: `RayleighQuotient.compute_class_conditioned()`
+- Hook management: `HookManager`, `PersistentHookManager`
+- `forward_with_activations()` for safe activation capture
 
 ### Changed
-
-#### **RayleighQuotient Improvements**
-- Added `regularization` parameter (default: 1e-6) for numerical stability
-- Automatic diagonal regularization prevents singular covariance matrices
-- Better handling of small batches and edge cases
-
-#### **BaseModelWrapper Enhancements**
-- Integrated `HookManager` for automatic cleanup
-- Improved activation capture with guaranteed hook removal
-- Added `__del__` to ensure cleanup on destruction
-
-#### **Package Version**
-- Version bumped: 0.1.0 → 0.2.0
-
----
+- RayleighQuotient: Added regularization parameter for numerical stability
+- BaseModelWrapper: Integrated HookManager for automatic cleanup
 
 ### Fixed
-
-- **Memory leaks:** HookManager ensures all hooks are properly removed
-- **Numerical stability:** Covariance regularization prevents crashes on small batches
-- **Hook accumulation:** Context managers prevent hook buildup over multiple captures
-
----
-
-### Performance
-
-- **Code duplication:** Reduced by ~40% via service layer
-- **Memory safety:** 100% - all hooks auto-managed
-- **Computation cost:** Redundancy/synergy scale as O(n·K), not O(n²)
-
----
-
-### Documentation
-
-#### **New Documentation**
-- `CODEBASE_IMPROVEMENTS.md` - 40+ pages of architectural recommendations
-- `IMPLEMENTATION_ROADMAP.md` - Week-by-week implementation plan
-- `unified_manuscript.tex` - Publication-ready research paper
-- `PROGRESS_REPORT.md` - Phase 1 progress tracking
-- `PHASE2_COMPLETE.md` - Phase 2 achievements
-- `SESSION_SUMMARY.md` - Overall session summary
-- `IMPLEMENTATION_COMPLETE.md` - Comprehensive overview
-- This CHANGELOG
-
-#### **Updated Documentation**
-- All new classes have comprehensive docstrings
-- Type hints added throughout
-- Examples demonstrate new features
-
----
-
-### Migration Guide
-
-#### **v0.1.0 → v0.2.0**
-
-**Good news:** All changes are **100% backward compatible!**
-
-#### **Existing code continues to work:**
-```python
-# This still works exactly as before
-from alignment.metrics import get_metric
-rq = get_metric('rayleigh_quotient')
-scores = rq.compute(inputs, weights)
-```
-
-#### **Recommended updates (optional):**
-
-**1. Use HookManager for safety:**
-```python
-# Before
-wrapper = BaseModelWrapper(model)
-outputs = model(inputs)
-activations = wrapper._activation_cache
-
-# After (safer)
-wrapper = BaseModelWrapper(model)
-outputs, activations = wrapper.forward_with_activations(inputs)
-# Hooks auto-cleaned!
-```
-
-**2. Use services for cleaner code:**
-```python
-# Before
-# Manual activation capture and metric computation
-# (50+ lines of boilerplate)
-
-# After
-from alignment.services import ActivationCaptureService, NodeScoringService
-
-capture = ActivationCaptureService(wrapper)
-data = capture.capture(input_batch)
-
-scorer = NodeScoringService(metrics={...})
-scores = scorer.compute_layerwise_scores(data, targets)
-# (5 lines!)
-```
-
-**3. Enable redundancy-aware pruning:**
-```python
-# Before
-rq_scores = rq_metric.compute(inputs, weights)
-mask = create_mask(rq_scores, amount=0.5)
-
-# After
-scores = scorer.compute_composite_scores(
-    inputs, weights, targets,
-    include_redundancy=True,
-    include_synergy=True
-)
-mask = MaskOperations.create_structured_mask(scores.composite, amount=0.5)
-```
-
----
-
-### Deprecations
-
-**None.** All existing APIs maintained.
-
----
-
-### Breaking Changes
-
-**None.** Full backward compatibility.
-
----
-
-## Future Plans
-
-### **v0.3.0 - Planned Features**
-- [ ] Backend abstraction (eager/JIT/CUDA)
-- [ ] Experiment component extraction
-- [ ] Comprehensive test suite (>85% coverage)
-- [ ] Recipe gallery with 5-7 examples
-- [ ] Performance optimizations
-
-### **v0.4.0 - Planned Features**
-- [ ] Transformer-specific metrics
-- [ ] LLM support (attention head analysis)
-- [ ] Custom CUDA kernels
-- [ ] Distributed covariance estimation
-
----
-
-## Contributors
-
-- Houman Safaai (Architecture, Implementation)
-- Andrew Landau (Original RQ implementation)
-
----
-
-## See Also
-
-- **Documentation:** `docs/` directory
-- **Examples:** `examples/` directory
-- **Tests:** `tests/` directory
-- **Roadmap:** `IMPLEMENTATION_ROADMAP.md`
-- **Paper:** `drafts/unified_manuscript.tex`
-
----
-
-## License
-
-See LICENSE file for details.
-
----
-
-Version 0.2.0
-
+- Memory leaks from accumulated hooks
+- Numerical stability in small batch scenarios
+
+## Core Features
+
+- Rayleigh Quotient and class-conditioned variants
+- Mutual Information (Gaussian and binning methods)
+- Information-theoretic metrics (redundancy, synergy, PID)
+- Similarity metrics (CKA, activation correlation)
+- Model wrappers for PyTorch models
+- Pruning strategies (magnitude, gradient, alignment-based)
+- Service layer for activation capture and scoring