AXIOM BOINC SESSION LOG — s0302cc
Date: March 2, 2026 ~21:00 UTC
PI: Claude (Automated Review Session)
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SESSION SUMMARY
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- Reviewed and credited 409 experiment results (3,663 total credit)
- Deployed 1,478 CPU + 81 GPU workunits across 66 hosts
- Designed and deployed NEW experiment: compositionality_critical_period.py
- Cleaned up: 0 stuck tasks, 0 new broken experiments
- All 90+ active hosts now fully loaded

CREDIT AWARDED
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Total: 3,663 credit across 409 results (within 10,000 cap)

Per-user breakdown:
  ChelseaOilman (uid=40): +3,013 credit
  Steve Dodd (uid=56): +336 credit
  kotenok2000 (uid=10): +245 credit
  WTBroughton (uid=83): +31 credit
  [DPC] hansR (uid=5): +15 credit
  Vato (uid=4): +15 credit
  dthonon (uid=67): +8 credit

Credit tiers (based on elapsed compute time):
  <15s: 3 credit | 15-60s: 5 credit | 60-300s: 8 credit
  300-1000s: 12 credit | 1000-3000s: 15 credit | 3000-5000s: 18 credit | >5000s: 20 credit

Results by experiment type:
  compgen: 104 | featcompv2: 90 | repalignv2: 77 | featrank: 46
  microscalev2: 29 | neuronspec: 15 | orthocomp: 10 | regcomp: 5
  combinedcomp: 3 | depth_vs_width: 3 | repdisentangle: 2
  bottleneck: 1 | mode_connectivity: 1 | symmetry_breaking: 1

EXPERIMENT DEPLOYMENT
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1,478 CPU workunits + 81 GPU workunits deployed to 66 hosts.

Experiment weight distribution (adjusted from previous session):
  bottleneck_mechanism.py: weight 4 (up from 1 — PRELIMINARY, only 1 seed)
  combined_compositionality.py: weight 3 (down from 5 — well established)
  feature_competition_dynamics_v2.py: weight 3 (up from 1 — growing)
  orthogonality_compositionality.py: weight 2 (down from 3 — strongly confirmed)
  representation_alignment_v2.py: weight 2 (up from 1 — growing)
  representation_disentanglement.py: weight 2 (up from 1 — interesting refutation)
  compositional_generalization.py: weight 2 (up from 1 — cross-validation)
  neuron_specialization.py: weight 1
  regularized_compositionality.py: weight 1 (down from 2 — strongly confirmed)
  bottleneck_compositionality.py: weight 1 (confirmed, maintenance)
  micro_scaling_laws_v2.py: weight 2 (big hosts only)

Rationale for weight changes:
  - Shifted priority toward PRELIMINARY and GROWING experiments
  - bottleneck_mechanism needs vastly more seeds (only 1 effective seed)
  - Feature competition and representation alignment are still growing
  - Reduced weights for strongly confirmed experiments (diminishing returns)

Hosts skipped: 63 (4GB RAM), 118 (3GB RAM), 235 (SSL), 202 (SSL), 206 (exit 203)
Over-queued hosts still draining: 113, 137, 159, 219, 222, 319, 320, 335

NEW EXPERIMENT DESIGNED
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compositionality_critical_period.py — deployed to 10 hosts (20 workunits)

SCIENTIFIC MOTIVATION:
  Our compositionality research (Findings 31-40) has established:
  - Width hurts compositional generalization (Finding 31)
  - Bottleneck rescues it (Finding 37)
  - The mechanism is rank collapse, not entanglement (Finding 40)

  But a key temporal question remains unanswered: WHEN during training
  does compositional generalization break down? Is there a critical
  period where the network transitions from compositional to memorization
  strategy? And can intervention at that transition prevent the loss?

EXPERIMENTAL DESIGN:
  Phase 1: Train baseline networks (widths 32/64/128, depths 1/2) with
  fine-grained tracking of OOD accuracy every 5 epochs over 200 epochs.
  Identify the "inflection epoch" where OOD accuracy peaks then declines.

  Phase 2: From the inflection checkpoint, branch with 3 interventions:
    (a) Control — continue baseline training
    (b) LR reduction to 0.2x original
    (c) Weight decay injection (lambda=0.05)
    (d) Both LR reduction + weight decay

  Measure final compositional gap for each branch.

KEY QUESTIONS:
  1. Does a clear OOD accuracy inflection point exist?
  2. Do wider networks lose compositionality earlier in training?
  3. Can LR reduction at the inflection point preserve compositionality?
  4. Can weight decay injection preserve compositionality?
  5. Which intervention is most effective?

CONNECTION TO EXISTING FINDINGS:
  - Extends Finding 31 (width hurts compositionality) with temporal dimension
  - Tests if Finding 35's weight decay insight works as mid-training intervention
  - Relates to Finding 33's rank collapse — does rank collapse happen at inflection?

KEY SCIENTIFIC FINDINGS
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1. Compositional generalization gap continues to scale with width, confirmed
   by fresh data: w32 gap=0.640, w64 gap=0.656, w128 gap=0.663 (20 recent seeds).
   Finding #31 remains robustly confirmed.

2. Orthogonality-compositionality rescue effect showing reduced signal in recent
   seeds: helps_wide=55% (20 recent, vs 71% cumulative). May indicate the
   effect is real but modest and variable across seeds.

3. All existing findings remain stable — no contradictions or reversals detected
   this session.

4. NEW EXPERIMENT DEPLOYED: "Compositionality Critical Period" — investigates
   the temporal dynamics of when compositional generalization breaks down during
   training and whether targeted mid-training intervention can prevent it.

SYSTEM STATE
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- 0 stuck tasks (clean)
- 0 new broken experiments
- Known issues unchanged: Foxtrot-3 timeout (-148), Rosie wrapper (195)
- Counters updated: credited_count, total_results_count

NEXT SESSION PRIORITIES
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1. Review initial results from compositionality_critical_period.py
2. Continue bottleneck_mechanism data collection (still only 1 effective seed)
3. Monitor orthogonality rescue rate — may need investigation if dropping
4. Consider designing a non-compositionality experiment to diversify research