Research Question

In Bio-Inspired Nanochat, is presynaptic vesicle depletion (RRP clamping) functionally distinguishable from an ordinary frequency penalty / logit bias? If yes, what minimal experiments separate the two?

Working Hypotheses

• H1 (Equivalence): RRP clamping is effectively a tuned frequency penalty; any apparent gains are regularization/cost tradeoffs.
• H2 (Mechanistic): RRP clamping creates context-/edge-dependent fatigue that changes attention dynamics in ways a token-count penalty can't reproduce.
• H3 (Misspecification): Any "wins" are artifacts (metric confound, sampling/seed effects, compute/capacity mismatch, or evaluation leakage).

Brenner Anchors

§103: "You've forgotten there's a third alternative… 'Both could be wrong'" — Third-alternative guard

§106: "Occam's Broom… the minimum number of facts have to be swept up under the carpet" — Minimize hidden contradictions

§160: "If you can't compute it you can't understand it." — Reconstruction standard

Expected Outputs

• Ranked discriminative tests (cheap → expensive)
• Evidence pack with relevant papers
• Assumption ledger for each hypothesis

Hypothesis Generator Response

Refined Working Hypotheses

H1 (Equivalence) - Strengthened

RRP clamping implements a stateful frequency penalty where the penalty coefficient decays over time (tau_rrp). This is mathematically equivalent to an exponentially-weighted moving average penalty on token frequencies.

Prediction: If we tune a frequency penalty with matching decay characteristics, outputs should be statistically indistinguishable.

H2 (Mechanistic) - Clarified

The key distinction would be attention-mediated suppression: RRP depletion could affect tokens differently based on their attention weights, not just their frequency. A high-attention token might deplete faster than a low-attention one with the same frequency.

Prediction: In prompts with identical token frequencies but different attention structures, RRP and frequency penalty should diverge.

H3 (Misspecification) - Operationalized

Before claiming any effect, we must control for:

• Random seed effects (run N>30 seeds)
• Temperature/sampling interactions
• Prompt length confounds

{"delta":{"operation":"ADD","section":"hypotheses","content":{"id":"H1.1","title":"Stateful frequency penalty equivalence","confidence":0.6}}}

Test Designer Response

Ranked Discriminative Tests

Test 1: Matched-Baseline Equivalence (Cost: Low)

Replace RRP clamping with an explicit frequency penalty tuned to match repetition rate on a small calibration prompt set; compare on held-out prompts.

Metrics: Repetition rate, perplexity, BLEU self-overlap Falsifies: H1 if significant divergence; H2 if no divergence

Test 2: Context-Sensitivity Probe (Cost: Medium)

Construct two prompts with identical token-frequency statistics but different attention patterns (verified via attention visualization).

Design:

• Prompt A: Linear narrative (sequential attention)
• Prompt B: Nested structure (hierarchical attention)
• Both have same unigram/bigram frequencies

Prediction under H2: RRP behaves differently; frequency penalty doesn't.

Test 3: Ablation Matrix (Cost: Medium)

Run 4 conditions: vanilla, presynaptic-only, vanilla+freq_penalty, presynaptic+freq_penalty

Detects: Double-counting effects, interaction terms

{"delta":{"operation":"ADD","section":"tests","content":{"id":"T1","title":"Matched-baseline equivalence","priority":1,"status":"proposed"}}}

{"delta":{"operation":"ADD","section":"tests","content":{"id":"T2","title":"Context-sensitivity probe","priority":2,"status":"proposed"}}}

Adversarial Critic Response

Critical Review

On H1 (Equivalence)

Weakness: The "stateful frequency penalty" framing assumes RRP depletion is monotonic in token frequency. But biological synaptic depression shows facilitation at certain frequencies. Has this been checked?

Required control: Plot RRP value vs. token frequency across different contexts. If non-monotonic, H1 fails.

On H2 (Mechanistic)

Weakness: "Attention-mediated suppression" is vague. Attention to what? The current token? Previous tokens? The RRP implementation doesn't obviously use attention weights.

Demand: Point to specific code where attention affects RRP. If it doesn't exist, H2 is unfounded speculation.

On Test 2

Problem: How do you guarantee "identical token-frequency statistics"? Even small deviations could explain any differences. Need statistical test for frequency distribution equivalence (e.g., chi-squared).

Expanded H3 Considerations

• Evaluation leakage: Are the metrics computed on the same data used to tune RRP parameters?
• Cherry-picking: How were example prompts selected? Were failures hidden?

{"delta":{"operation":"ADD","section":"objections","content":{"id":"O1","target":"H2","severity":"major","description":"No evidence attention affects RRP in codebase"}}}

Research Brief v1: Bio-Inspired Nanochat

Executive Summary

This session investigates whether presynaptic vesicle depletion (RRP clamping) in Bio-Inspired Nanochat provides genuine mechanistic benefits over simpler frequency penalties.

Current Hypothesis Slate

Discriminative Test Queue

Open Objections

• O1 (Major): No evidence that attention weights affect RRP in the actual codebase. H2 may be unfounded.

Next Steps

1. Code audit: Search for attention-RRP coupling
2. Run Test 1 with N=30 seeds
3. Address O1 before investing in Test 2

Brenner Protocol Compliance

• ✅ Third alternative included (H3)
• ✅ Discriminative tests ranked by cost
• ⚠️ Need to verify "compute it to understand it" for H2

Compiled from 3 agent contributions. Version 1.