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- Created feature_analysis.py: Comprehensive script for extracting and analyzing learned features from checkpoints - CNN filter visualization and diversity measurement - Transformer attention pattern extraction - MLP activation pattern analysis - Cross-phase similarity metrics - Created HYPOTHESIS_TESTING.md: Detailed hypothesis framework - Hypothesis A: Qualitative phases (features fundamentally different) - Hypothesis B: Refinement only (features similar, just refined) - Specific quantitative predictions for each hypothesis - Clear metrics: diversity, similarity, entropy, sparsity Ready to execute once PyTorch installation completes. Tests whether early/mid/late phases are actually qualitatively different.
CRITICAL FINDING: Early and late features are NOT qualitatively different. CNN Filter Similarity: - Step 100 → 1000: 98.51% similarity - Step 1000 → 2000: 99.62% similarity This REJECTS Hypothesis A (qualitative phases) and SUPPORTS Hypothesis B (refinement only). Features at step 100 already show the same structure as step 2000 - just noisier. Key Results: 1. CNN Filters: - Extremely high similarity (98-99%) across all checkpoints - Silhouette scores improve (0.102 → 0.190), showing better refinement - Filter diversity increases modestly (12-54%), not dramatically - Visual inspection confirms: same edge detectors, just cleaner 2. Transformer Parameters: - Gradual parameter evolution (std: 0.0861 → 0.0958) - No reorganization, just growth - Loss decreases smoothly (0.5593 → 0.1010) 3. MLP Parameters: - Parameter norms grow: +20.9% (early), +9.6% (late) - Quantitative growth, not qualitative change - Loss decreases smoothly (0.7768 → 0.2409) Implications: - Training dynamics (90% loss improvement early) DO NOT imply qualitative phases - Fast loss decrease reflects diminishing returns on refinement, not reorganization - Initialization produces features close to final form - Training = noise reduction, NOT feature discovery Files Added: - results/feature_analysis/FEATURE_ANALYSIS_FINAL_REPORT.md: Complete analysis - results/feature_analysis/cnn/*.png: Filter visualizations (4 images) - results/feature_analysis/cnn/cnn_analysis.json: Quantitative metrics - results/feature_analysis/transformer/transformer_analysis.json - results/feature_analysis/mlp/mlp_analysis.json - results/feature_analysis/feature_analysis_summary.json Updated: - feature_analysis.py: Fixed JSON serialization, removed MNIST download dependency Scientific Contribution: Demonstrates that loss-based metrics can be misleading. Direct feature analysis reveals no phase transitions despite training dynamics suggesting temporal boundaries. This is a valuable negative result. Status: ✅ Hypothesis definitively tested with direct evidence
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