AXIOM BOINC SESSION RESULTS
Session timestamp: 2026-03-04 09:27 (America/Denver)
Scope: Part 1 validation/credit + Part 2 deploy/research consolidation

PART 1: RESULTS REVIEWED AND CREDIT AWARDED
- Reviewed 273 completed success results from the latest validation pass (all parseable experiment_result payloads; 0 parse failures, 0 error payloads).
- Credit awarded this session: 3,279 total across 273 results (CPU 261, GPU 12), within the 10,000 cap.
- Runtime profile of reviewed rows: min 49.06s, median 846.10s, max 1221.28s.
- Top credited users this pass: ChelseaOilman (+2374), Orange Kid (+384), Steve Dodd (+240), PyHelix (+127), WTBroughton (+66).

CLEANUP AND RELIABILITY ACTIONS
- Dead-host stuck-task cleanup (>12h running and >6h no contact): 0 aborted.
- Hard-ceiling cleanup (>48h): 0 aborted.
- Broken-experiment screening (CPU/GPU split): no high-failure multi-host crash pattern; no broad aborts applied.
- Counters after run: credited_count.txt=5650; total_results_count.txt=4896; uncredited completed success rows at cutoff=74.

PART 2: DEPLOYMENT SUMMARY
CPU deployment
- Host-targeted queue fill executed for active CPU hosts with per-host goal of ~3x CPU queued work.
- CPU hosts seen: 81 total; low-RAM skips (<6GB): 2; eligible hosts targeted: 79.
- CPU workunits created: 2937.
- CPU scripts deployed: wd_batchnoise_interaction.py, wd_labelsmooth_interaction.py.

GPU deployment checkpoint
- GPU deployment pass used scripts: wd_curvature_trigger_gpu.py and wd_timing_scale_gpu.py.
- The captured Part 2 run log was interrupted before final GPU totals were printed; live queue checkpoint confirms target GPU lines are active.
- Current queued GPU results for those scripts: 19 across 6 hosts.
- Script split in queue: wd_timing_scale_gpu (11 queued across 6 hosts), wd_curvature_trigger_gpu (8 queued across 3 hosts).

NEW EXPERIMENT DESIGN + NOVELTY CHECK NOTES
- New script added in Part 2: wd_batchnoise_interaction.py.
- Mechanistic/interaction hypotheses used:
  1) WD timing x label smoothing interaction (attenuation/amplification of late-WD ICP behavior).
  2) Curvature-triggered WD mechanism test (trigger at curvature stabilization vs fixed late start).
  3) WD timing x batch-noise interaction (small-batch noise expected to strengthen late-WD benefit).
- Novelty-check web searches documented in run log included:
  - 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
  - arxiv 1711.05101 decoupled weight decay regularization
- Outcome of novelty check: no direct prior match found for the exact three-way interaction framing used here (timing x label smoothing / timing x noise / curvature-trigger trigger rule), so deployment proceeded.

KEY SCIENTIFIC FINDINGS
1. Latest validated batch quality remained high: 273/273 reviewed uploads parsed as structured experiment_result outputs, with stable runtime concentration near ~846s median.
2. Delay-driven ecology/control families dominated throughput in this validation window (toggle/cascade/tritrophic/lorenz96/metapop corridor lines), with no evidence of a broad broken experiment family requiring shutdown.
3. Part 2 now actively probes WD mechanism interactions (timing x label smoothing, timing x batch-noise, curvature-triggered onset), extending from established ICP observations toward causal and interaction-level tests.