Axiom BOINC Session Log (Parts 1+2)
Session timestamp: 2026-03-04 06:07 (America/Denver)
Source logs: validate_2026-03-04_0605.txt + run_2026-03-03_1352.log

RESULTS REVIEWED AND CREDIT AWARDED (Part 1)
- Reviewed completed, previously uncredited exp_% rows: 336.
- Outcome mix: success(outcome=1)=255; non-success(outcome=6)=81.
- App mix: CPU(appid=1)=315; GPU(appid=2)=21.
- Credit awarded this session: 2,093 total across 336 results (under 10,000 cap).
- Top awarded users this pass: Amapola (+1302), Orange Kid (+349), ChelseaOilman (+266), Steve Dodd (+96).
- Upload artifact audit for reviewed rows: missing=336, experiment_result=0, error=0, invalid_json=0.

STUCK/BROKEN TASK CLEANUP
- Broken-prefix active-task checks: oscillatory_roughchannel_lbm_resonance=0, abx_cycle_hgt_delay_resonance=0, abx_cycle=0, potts_pulse_anneal_resonance=0, spatial_pgg_delay_fatigue=0.
- Broken-prefix abort actions performed: 0 rows.
- Dead-host stuck-task cleanup (>12h): 0 aborted.
- Hard-ceiling cleanup (>48h): 0 aborted.
- Retirement pass in Part 2 found over-completed lines but unsent=0 for listed candidates; ABORT_TOTAL=0.

DEPLOYMENT SUMMARY (Part 2)
CPU deployment
- Host-targeted CPU queue fill executed to ~3x CPU queue depth.
- CPU hosts seen: 81.
- Hosts skipped for RAM<6GB: 2.
- CPU workunits created: 2937.
- CPU scripts used: wd_batchnoise_interaction.py, wd_labelsmooth_interaction.py.

GPU checkpoint
- GPU deployment script launched with scripts: wd_curvature_trigger_gpu.py, wd_timing_scale_gpu.py.
- The recorded run log was interrupted during GPU execution (^C), so final GPU_HOSTS_FOUND/GPU_WU_CREATED totals were not emitted in that log.
- Checkpoint status from this run log: GPU hosts counted in output=not recorded; GPU workunits counted in output=not recorded.

NEW EXPERIMENT DESIGN + NOVELTY CHECK (Part 2)
- New experiment added: wd_batchnoise_interaction.py (uploaded and py_compile OK).
- Hypothesis: late weight decay should improve generalization more under high gradient-noise (small batch) than low-noise (large batch) training.
- Novelty-check documentation (queries run during design):
  - 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
- Non-duplication rationale: this introduces explicit batch-noise x late-WD interaction testing beyond existing fixed-timing and label-smoothing interaction lines.

KEY SCIENTIFIC FINDINGS
1. No new retrievable experiment_result JSON payloads were found in this validation pass (0/336 reviewed rows), indicating the reliability bottleneck remains the primary blocker to extracting fresh quantitative science from completed traffic.
2. Delay-family workloads continue to dominate completed rows (notably lorenz96_delay_assimilation_regime_shift, climate_delay_carbon_hysteresis_window, cahnhilliard_delay_feedback_window, interdep_flow_memory_shedding_tradeoff), while this pass still yielded no recoverable payloads.
3. GPU participation remains present but modest in the validated batch (21/336 rows), so current throughput and scientific signal are still primarily constrained by CPU-side completion reliability.
4. A new mechanism-focused line (wd_batchnoise_interaction) was introduced to test whether gradient-noise regime mediates late-WD gains, extending prior WD timing and label-smoothing interaction hypotheses.

OPERATIONAL NOTES
- Website counters after Part 1: credited_count=2722, total_results_count=2703.
- No new script bug fix was applied in Part 1; known broken families had no active rows to abort during this pass.