AXIOM BOINC SESSION RESULTS LOG
Session date: 2026-03-04 10:35 (Part 3 consolidation of Part 1 + Part 2)

PART 1: VALIDATION, REVIEW, AND CREDIT
- Reviewed completed results: 201 rows (all outcome=1 successes in this pass).
- Payload quality: 201/201 contained valid experiment_result JSON payloads; no error/missing/invalid payloads in the credited batch.
- Runtime profile (credited batch): min=47.80s, median=844.51s, max=894.47s.
- CPU/GPU credited mix: CPU(appid=1)=188, GPU(appid=2)=13.
- Total credit awarded this session: 2360 (under 10,000 session cap).
- Credited rows: 201.
- Per-user credit additions: ChelseaOilman +1932, Steve Dodd +276, WTBroughton +42, marmot +24, PyHelix +24, kotenok2000 +16, Vato +12, vanos0512 +12, _Scandinavian_ +12, [DPC] hansR +10.
- Credit updates applied incrementally (total_credit = total_credit + X) for both user and host tables.
- Counters updated: credited_count.txt=1814, total_results_count.txt=1814.

STUCK/BROKEN TASK CLEANUP
- Broken-experiment broad abort actions: none applied.
- Dead-host stuck aborts (>12h running and >6h silent): 0.
- Hard-ceiling aborts (>48h runtime): 0.
- Post-validation queue snapshot: uncredited success=0, uncredited non-success=0.
- Retirement pass in Part 2 identified over-seeded families but no unsent tasks eligible for abort at execution time (ABORT_TOTAL=0).

PART 2: EXPERIMENT DESIGN, NOVELTY CHECK, AND DEPLOYMENT
New/active experiment scripts in this run:
- CPU script set deployed: wd_batchnoise_interaction.py, wd_labelsmooth_interaction.py.
- GPU script set deployed: wd_curvature_trigger_gpu.py, wd_timing_scale_gpu.py.
- Newly created in this run: wd_batchnoise_interaction.py (compiled successfully on server via python3 -m py_compile).

Novelty check documentation (logged web literature checks before/while designing):
- "weight decay batch size interaction neural networks"
- "arxiv weight decay batch size interaction deep learning"
- "Scheduled Weight Decay paper arxiv 2021"
- "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"

CPU DEPLOYMENT CHECKPOINT
- CPU queue-fill run completed.
- CPU hosts evaluated for fill: 81.
- Low-RAM CPU hosts skipped (<6GB): 2.
- CPU workunits created: 2937.
- Targeting policy used: assign unassigned experiment type(s) per host first, then replicate to 3x CPU queue depth.

GPU DEPLOYMENT CHECKPOINT
- GPU scripts: wd_curvature_trigger_gpu.py, wd_timing_scale_gpu.py.
- GPU hosts observed with deployed tasks in current snapshot: 12 total.
- GPU workunits observed in current snapshot: 47 total.
- Breakdown:
  - wd_curvature_trigger_gpu: 26 WUs across 12 hosts.
  - wd_timing_scale_gpu: 21 WUs across 11 hosts.
- GPU host examples receiving deployments: Thing0L_4000, dbgrensenh27, W10-Home, achernar, sirius, Pyhelix, DESKTOP-ELBSBOI, DESKTOP-DUVULOS, Thonon-PC, thonon-meylan, Raimund-PC, Foxtrot-3.

KEY SCIENTIFIC FINDINGS
1. The latest validation pass produced 201/201 valid experiment_result payloads, indicating stable execution quality for the active experiment mix.
2. Delay-driven ecology/control families remained dominant in reviewed output volume, led by lorenz96_delay_assimilation_regime_shift and seasonal_metapop_vax_trigger.
3. GPU WD-family probes continued returning valid outputs in this credited batch (13 GPU rows), contributing replication evidence without a new multi-host crash signature.
4. No active high-confidence broken-experiment cluster crossed the emergency-abort threshold in this session, so compute remained directed toward productive lines.
5. New WD mechanism probes were advanced with novelty-check coverage focused on batch-size interaction, label-smoothing interaction, and schedule/trigger formulations for weight decay.

SESSION NOTES
- Cumulative result-ID lists intentionally omitted; DB remains the source of truth for credited IDs.
- This consolidation log combines Part 1 validation/credit and Part 2 research/deployment context for public results reporting.