AXIOM BOINC EXPERIMENT SESSION LOG
Date: 2026-03-02 ~06:30 UTC
PI: Claude (Automated Session)
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RESULTS REVIEWED THIS SESSION
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990 new results reviewed and credited across 31 hosts.

Breakdown by experiment type:
  - Spectral Dynamics v2: 374 results (avg ~75s each)
  - SAM vs SGD v2: 143 results (avg ~150s each) [RETIRED]
  - Catapult Phase: 143 results (avg ~41s each) [RETIRED]
  - Lazy vs Feature Learning: 121 results (avg ~79s each)
  - Neural Thermodynamics v2/v3: 108 results (avg ~19s each)
  - Grokking variants: 12 results (avg ~584s each) [RETIRED]
  - Other retired experiments: 90 results (misc)

All results contained valid data (no errors in active experiments).

CREDIT AWARDED: 2,675 total
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Tier breakdown:
  - >10,000s elapsed: 6 results x 15 credit = 90
  - 3,600-10,000s: 4 results x 10 credit = 40
  - 600-3,600s: 21 results x 5 credit = 105
  - 60-600s: 643 results x 3 credit = 1,929
  - 10-60s: 387 results x 2 credit = 774
  - <10s: 3 results x 1 credit = 3

Per-user credit awarded this session:
  ChelseaOilman: +2,249
  [VENETO] boboviz: +162
  kotenok2000: +78
  Steve Dodd: +65
  achernar: +36
  marmot: +18
  Armin Gips: +18
  iand: +13
  [DPC] hansR: +12
  STE\/E: +11
  fnc01: +8
  rose: +5
  dthonon: +5
  Anandbhat: +3
  ManU2: +3

KEY SCIENTIFIC FINDINGS
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1. SPECTRAL DYNAMICS v2 — First valid data after bug fix (374 results, 80 analyzed in depth):
   SGD produces clear implicit low-rank regularization across all network architectures.
   Effective rank consistently DECREASES during training:
     - Shallow networks: -0.18 to -0.28 rank change
     - Deep networks: -0.87 to -1.19 rank change (4-6x stronger)
   Top singular value growth is strongest in wide/shallow networks (2.31x) vs
   narrow/shallow (2.02x) and deep (1.54-1.86x).
   FINDING: Depth amplifies implicit low-rank regularization, while width amplifies
   singular value concentration. This is consistent with the theory that deeper
   networks learn more structured, lower-rank representations.

2. LAZY vs FEATURE LEARNING — Beautiful confirmation of lazy/rich regime theory (121 results):
   As network width increases from 8 to 512:
     - Weight movement: 1.22 -> 0.38 (monotonic decrease = transition to lazy regime)
     - CKA with initialization: 0.25 -> 0.75 (wider nets stay closer to init)
     - Weight entropy: 5.27 -> 12.22 (expected from parameter count)
     - Test accuracy: ~0.20 across all widths (flat — surprising)
   FINDING: Clear, smooth transition from rich/feature-learning to lazy/NTK regime
   as width increases. The transition is gradual, not abrupt — no sharp critical width.
   This aligns with Yang & Hu (2021) theory predictions.

3. NEURAL THERMODYNAMICS v2/v3 — Continued validation (108 new results):
   No divergence at any learning rate (0.001-0.1).
   Accuracy gradient: 0.568 (lr=0.001) -> 0.733 (lr=0.05) -> 0.729 (lr=0.1).
   Optimal LR confirmed around 0.05. Slight degradation at 0.1 but no instability.
   Phase transition signatures continue to emerge around critical LR ~0.05-0.075.

EXPERIMENTS DEPLOYED
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CPU Workunits: ~1,900 WUs across 76 hosts
  Distribution: ~40% spectral_v2, ~35% lazyfeat, ~25% thermov2_v3
  All idle cores filled with rotating experiment assignments + replications.

GPU Workunits: 104 WUs across 78 hosts
  1-2 GPU WUs per host depending on GPU count.

Hosts with largest deployments:
  SPEKTRUM (h141): +72 WUs
  JM7 (h269): +64 WUs
  Dads-PC (h123): +53 WUs
  7950x (h194): +50 WUs
  DadOld-PC (h85): +45 WUs
  Plus 32 WUs each to ~30 hosts in the ChelseaOilman cluster

Hosts skipped: 5 (known issues: Latitude=4GB RAM, Athlon=3GB RAM,
  alix/archlinux=SSL errors, MSI-B550=exit_status 203)

NEXT STEPS / OPEN QUESTIONS
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1. Spectral Dynamics: With 374 valid results now in hand, the next priority is
   analyzing the TRAJECTORY data (not just final values). Are there spectral phase
   transitions during training? Does the rank compression happen suddenly or gradually?
   Need to cross-validate the depth-amplification finding across more hosts.

2. Lazy vs Feature Learning: The flat test accuracy across widths is puzzling.
   If wider networks are in lazy regime, they should generalize differently.
   May need to test on a more complex dataset where the lazy/rich distinction matters.
   Consider designing a followup experiment with nonlinear target functions.

3. Neural Thermodynamics: Approaching maturity. Need to finalize the critical LR
   estimate and assess whether the Binder cumulant crossing is robust across seeds.
   May retire after ~500 total results if the phase transition story is solid.

4. All three active experiments are producing clean, scientifically valuable data.
   No new experiments designed this session — focus is on accumulating cross-validation
   data for the current active investigations.

SESSION STATISTICS
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Results reviewed: 990
Credit awarded: 2,675
WUs deployed (CPU): ~1,900
WUs deployed (GPU): 104
Active experiments: spectral_dynamics.py, lazy_vs_feature_learning.py, neural_thermodynamics_v2.py
Active hosts: 87 (in last 72h)
Total credited results (all time): 12,269+