AXIOM BOINC SESSION RESULTS (PART 3 CONSOLIDATED)
Session timestamp: 2026-03-04 02:19 (America/Denver)
Source logs: validate_2026-03-03_2353.txt, run_2026-03-03_1352.log, findings_summary.txt

RESULTS REVIEWED AND CREDIT AWARDED (PART 1)
- Credited pass 1: 52 results, 217 total credit.
- Credited pass 2: 31 results, 154 total credit.
- Total credited this session: 83 results, 371 total credit (<= 10,000 cap).
- Session credit recipients:
  - Steve Dodd: +152
  - ChelseaOilman: +113
  - kotenok2000: +45
  - Anandbhat: +40
  - PyHelix: +17
  - [DPC] hansR: +4

STUCK/BROKEN TASK CLEANUP
- Dead-host >12h running aborts: 0
- Hard ceiling >48h running aborts: 0
- Broken-prefix active aborts applied (unsent+in-progress):
  - metapop_corridor_delay_forecast: 170
  - rps_delay_jitter_adaptive_mobility: 110
  - wd_mixup_interaction: 31
  - gpu_matmul_stress_gpu: 27
  - Total broken-prefix aborts: 338

DEPLOYMENT SUMMARY (PART 2)
CPU deployment
- Targeted CPU queue fill (latest run log): hosts seen 81, skipped low-RAM hosts (<6 GB): 2.
- CPU workunits created in latest run: 2,937.
- CPU scripts used in latest run: wd_batchnoise_interaction.py, wd_labelsmooth_interaction.py.

GPU deployment
- GPU deployment scripts invoked in latest run: wd_curvature_trigger_gpu.py, wd_timing_scale_gpu.py.
- Latest run log ended during GPU deployment before final counters were emitted.
- Most recent completed GPU checkpoint (same session context from findings_summary/complete deploy run):
  - GPU hosts with idle capacity detected: 80
  - GPU workunits deployed: 629 (313 targeted + 316 untargeted fallback)
  - Primary GPU deployment script: wd_curvature_trigger_gpu.py

NEW EXPERIMENTS DESIGNED + NOVELTY CHECK DOCUMENTATION
1) wd_labelsmooth_interaction.py (CPU)
- Hypothesis: label smoothing changes the late-WD inverse critical-period effect.
- Novelty check query: "weight decay label smoothing interaction neural networks"
- Prior-art references reviewed: arXiv:2010.16402, 1706.05350, 1910.00482, 1711.05101.
- Novel angle documented: direct WD-timing x label-smoothing interaction in one controlled ICP setup was not found as an established result.

2) wd_curvature_trigger_gpu.py (GPU-aware)
- Hypothesis: effective WD onset is gated by curvature stabilization.
- Novelty check query: "adaptive weight decay schedule deep learning"
- Prior-art references reviewed: arXiv:2111.09764, 2001.04796, 2404.03672, 2201.00519.
- Novel angle documented: curvature-triggered onset vs fixed late WD as a causal ICP mechanism test.

KEY SCIENTIFIC FINDINGS
1. Recent completed uploads remain strongly science-bearing in QC samples (Part 1 validation found 49/49 valid experiment_result payloads in uncredited-success sample).
2. Several high-failure prefixes still show outcome/status mismatch signatures (outcome=5 with exit_status=0 and often valid payloads), reinforcing validator/outcome-classification as the likely bottleneck rather than pure runtime crash.
3. Over-seeded/low-yield lines were actively constrained while deployment pivoted toward mechanism-focused WD interaction studies, including label-smoothing interaction and curvature-triggered WD onset.
4. GPU queue strategy remains effective when combining targeted placement with fallback queueing; recent checkpoint recorded 80 GPU hosts and 629 deployed GPU WUs for new mechanism tests.

NOTES
- Cumulative result ID lists intentionally omitted; credited/uncredited truth is tracked in the BOINC database.