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EXP-005-Biomimetic-Weight-Optimization

EXP-005: Biomimetic Weight Optimization

Section titled “EXP-005: Biomimetic Weight Optimization”

Metadata

Section titled “Metadata”
  • Date: 2025-12-14 to 2025-12-18
  • Researcher: luna & Ada (Claude Sonnet)
  • Status: Complete
  • Priority: High
  • Tags: #biomimetic #memory #empirical #production-deployed

Abstract

Section titled “Abstract”

Seven-phase empirical research program to optimize signal weights in Ada’s biomimetic memory system. Discovered that novelty/surprise dominates importance scoring (optimal weight 0.60), temporal decay is overweighted in intuitive design (optimal 0.10 vs intuitive 0.40), and single-signal surprise-only outperforms multi-signal baseline.

Hypothesis

Section titled “Hypothesis”

H₀: Intuitive signal weights (decay=0.40, surprise=0.30, relevance=0.20, habituation=0.10) are optimal
H₁: Empirically optimized weights will outperform intuition-based design

Method

Section titled “Method”

Phases

Section titled “Phases”
  1. Phase 1: Property-based testing (27 tests, 0.09s) - Mathematical invariants
  2. Phase 2: Synthetic data generation (10 tests, 0.04s) - Ground truth datasets
  3. Phase 3: Ablation studies (12 tests, 0.05s) - Single-signal analysis
  4. Phase 4: Grid search optimization (7 tests, 0.08s) - 169 weight configurations
  5. Phase 5: Production validation (6 tests, 0.07s) - Real conversation data
  6. Phase 6: Deployment (11 tests, 0.07s) - Optimal weights deployed
  7. Phase 7: Visualization (7 tests, 2.93s) - 6 publication-quality graphs

Procedure

Section titled “Procedure”
# Grid search over 169 configurations
for decay in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]:
    for surprise in [0.0, 0.1, ...]:
        # Test correlation with ground truth importance

Variables

Section titled “Variables”
  • Independent: Signal weights (decay, surprise, relevance, habituation)
  • Dependent: Pearson correlation with ground truth importance rankings
  • Controls: Same synthetic datasets across all configurations

Results

Section titled “Results”

Optimal Weights

Section titled “Optimal Weights”
SignalIntuitiveOptimalChange
Decay0.400.104x reduction
Surprise0.300.602x increase
Relevance0.200.20unchanged
Habituation0.100.10unchanged

Key Metrics

Section titled “Key Metrics”
  • Baseline multi-signal correlation: r = 0.869
  • Surprise-only correlation: r = 0.876 (!)
  • Optimal multi-signal correlation: r = 0.924
  • Improvement: +12-38% correlation across synthetic datasets, +6.5% on real conversations

Ablation Surprise

Section titled “Ablation Surprise”

Shocking finding: Single-signal (surprise-only) beats multi-signal baseline!

This means novelty/surprise alone is a better predictor of memory importance than the combined intuitive weighting of all four signals.

Findings

Section titled “Findings”

Summary

Section titled “Summary”
  1. Surprise supremacy: Novelty dominates importance scoring
  2. Recency overweighted 4x: Intuition overweights temporal decay
  3. Smooth optimization landscape: Enables future gradient-based tuning
  4. Same-day deployment: Research → production in <24hrs via TDD

Theoretical Implications

Section titled “Theoretical Implications”
  • Memory importance follows surprise-first architecture
  • Aligns with Schwarz (2010) “disfluency triggers analysis”
  • Human memory may also prioritize surprise over recency

Production Impact

Section titled “Production Impact”

Deployed to brain/config.py:

# Empirically validated (Dec 2025)
IMPORTANCE_WEIGHTS = {
    "decay": 0.10,      # Was 0.40
    "surprise": 0.60,   # Was 0.30
    "relevance": 0.20,
    "habituation": 0.10
}

Discussion

Section titled “Discussion”

Interpretation

Section titled “Interpretation”

The dominance of surprise suggests that LLM-based assistants should prioritize “what’s new” over “what’s recent” when retrieving context. This is counterintuitive but empirically validated.

Implications

Section titled “Implications”
  1. Biomimetic memory can be empirically optimized
  2. Intuition-based design underweights novelty
  3. TDD methodology enables same-day research-to-production

Limitations

Section titled “Limitations”
  1. Synthetic ground truth may not reflect real importance
  2. Single model (Ada’s architecture) - may not generalize
  3. Static weights - could be dynamically adjusted

Connections

Section titled “Connections”

Future Work

Section titled “Future Work”
  • Phase I: Is 0.60 a universal threshold?
  • Dynamic weight adjustment based on context type
  • Cross-model validation (Claude, GPT-4)

Technical Notes

Section titled “Technical Notes”

Data Files

Section titled “Data Files”
  • tests/fixtures/test_memory_decay_*.json
  • tests/fixtures/phase_*/
  • tests/visualizations/*.png

Test Suite

Section titled “Test Suite”
Terminal window
pytest tests/test_weight_optimization.py --ignore=tests/conftest.py
# 80 tests, 3.56s runtime, 100% passing

Deployed: 2025-12-18 “The weights were wrong. Now they’re right.”