AXIOM BOINC SESSION LOG - PART 3 SAVE SUMMARY
Session timestamp: 2026-03-04 13:36 -07:00
Source logs: validate_2026-03-04_1331.txt, run_2026-03-03_1352.log

PART 1 VALIDATION AND CREDIT SUMMARY
- Reviewed and credited: 246 completed success results (all payload-valid experiment_result JSON).
- Incremental credit awarded: 4890 total credit (session cap 10000).
- Top credited contributors this session: ChelseaOilman, PyHelix, _Scandinavian_, Steve Dodd, kotenok2000.
- Dominant credited experiment families: tritrophic_delay_harvest_resilience, metapop_corridor_delay_forecast, grayscott_delay_pulse_feedback, kuramoto_delay_noisy_control, multiplicative_reset_sign_stability, lorenz96_delay_assimilation_regime_shift, fisher_delay_adaptive_culling, abx_cycle_hgt_delay_resonance.
- Throughput profile remained stable: min 67.990128 s, median 844.167149 s, max 888.661537 s.

CLEANUP AND STABILITY ACTIONS
- Stuck dead-host tasks aborted (>12h run, >6h no contact): 0.
- Hard-ceiling aborts (>48h run): 0.
- Broken-experiment broad aborts: none in Part 1.
- Part 2 retirement pass executed; retirement candidates were reviewed and ABORT_TOTAL=0 because unsent backlog for those candidates was already zero.

PART 2 DEPLOYMENT SUMMARY
CPU DEPLOYMENT
- Host-targeted CPU queue-fill to ~3x CPU slots was run.
- CPU hosts scanned: 81.
- Hosts skipped for low RAM (<6 GB): 2.
- CPU workunits created in this deployment pass: 2937.
- CPU scripts used: wd_batchnoise_interaction.py, wd_labelsmooth_interaction.py.

GPU DEPLOYMENT CHECKPOINT
- Planned GPU scripts: wd_curvature_trigger_gpu.py, wd_timing_scale_gpu.py.
- Run log shows GPU deployment command was started but interrupted before completion output was emitted.
- Verified checkpoint after run: 0 active hosts and 0 workunits currently matching these two GPU deployment name patterns.
- Operational interpretation: no confirmed new GPU queue-fill from this interrupted pass.

NEW EXPERIMENTS DESIGNED (WITH NOVELTY CHECK DOCUMENTATION)
1. wd_batchnoise_interaction.py
- Hypothesis: late weight-decay gain should be stronger under small-batch gradient-noise conditions than large-batch conditions.
- Novelty check recorded in run log via web/literature searches including:
  - "site:arxiv.org weight decay label smoothing interaction"
  - "site:arxiv.org adaptive weight decay deep neural networks"
  - "site:arxiv.org batch size weight decay generalization"
  - "\"weight decay\" \"batch size\" \"schedule\" neural networks"
  - "arxiv 1711.05101 decoupled weight decay regularization"
- Script upload and server syntax validation were completed (python3 -m py_compile -> OK).

KEY SCIENTIFIC FINDINGS
1. This session added 246 validated science-bearing experiment_result payloads with no credited payload corruption, strengthening confidence in current delay/resonance and WD-track data streams.
2. Runtime behavior stayed tightly centered near an ~844 s median across mixed experiment families, indicating stable volunteer throughput and predictable batch timing.
3. The strongest replication volume in this credited batch came from tritrophic delay harvest resilience, metapop corridor delay forecast, and grayscott delay pulse feedback, improving statistical support for those lines.
4. No active nonzero-elapsed multi-host crash signature was detected in Part 1, so no broad experiment shutdown was required.
5. Research direction advanced with a new interaction-focused WD experiment design (batch-noise x late-WD interaction), with explicit novelty search evidence logged before deployment attempts.