AXIOM BOINC SESSION RESULTS LOG
Session timestamp: 2026-03-04 07:04 (America/Denver)
Workflow: Part 3 Save & Upload (compiled from Part 1 + Part 2 logs)

PART 1 VALIDATION / CREDIT SUMMARY
- Source log: validate_2026-03-04_0700.txt
- Results reviewed this session: 1409 completed terminal exp_* rows (bulk-reviewed by experiment/type tiers).
- Credit awarded this session: 2876.00 total credit across 1409 rows.
- Mix credited: success=299, failed=1110, CPU(appid=1)=1334, GPU(appid=2)=75.
- Upload payload audit in this pass: experiment_result=0, error=0, missing=1409, invalid_json=0.

STUCK/BROKEN TASK CLEANUP
- Broken queue halted: rw_reset_fatigue_nonrenewal active queue reduced to 0 due reproducible immediate-failure pattern.
- Dead-host stuck-task cleanup (>12h running + >6h no contact): 0 aborted.
- Hard-ceiling cleanup (>48h): 0 aborted.

PART 2 DEPLOYMENT SUMMARY (from run_2026-03-03_1352.log + live checkpoint)
CPU deployment
- Host-targeted CPU fill pass scanned 81 active CPU hosts; skipped 2 hosts below 6 GB RAM.
- CPU workunits created in pass: 2937.
- CPU scripts used: wd_batchnoise_interaction.py, wd_labelsmooth_interaction.py.
- Targeting model used in run: fill toward ~3x CPU core queue per eligible host.

GPU deployment checkpoint
- GPU scripts used: wd_curvature_trigger_gpu.py, wd_timing_scale_gpu.py.
- Run log shows GPU deployment pass started as mandatory separate appid=2 phase.
- Live checkpoint (post-run) for these GPU scripts: 7 active GPU hosts carrying 13 active WUs total.
- Active GPU host IDs at checkpoint: 355, 287, 340, 9, 159, 299, 341.
- Active GPU WU split at checkpoint: wd_curvature_trigger_gpu=5, wd_timing_scale_gpu=8.

NEW EXPERIMENTS / NOVELTY CHECK DOCUMENTATION
- New experiment script added and compiled: wd_batchnoise_interaction.py.
- Hypothesis focus: interaction between batch-noise regime and late weight-decay benefit magnitude.
- Literature/novelty search queries logged during Part 2:
  1. site:arxiv.org weight decay label smoothing interaction
  2. site:arxiv.org adaptive weight decay deep neural networks
  3. site:arxiv.org batch size weight decay generalization
  4. "weight decay" "batch size" "schedule" neural networks
  5. arxiv 1711.05101 decoupled weight decay regularization
- Novel angle documented in deployment session: direct interaction test (late WD gain under small vs large batch noise regimes) and dedicated GPU trigger/timing follow-up lines.

RETIREMENT / QUEUE MANAGEMENT ACTIONS
- Retirement candidate sweep executed in Part 2.
- Candidate families identified with high completed counts; unsent abort action in that pass: ABORT_TOTAL=0.

KEY SCIENTIFIC FINDINGS
1. Validation pass found no newly retrievable experiment_result payloads among 1409 credited rows (all missing payload artifacts), reinforcing the persistent DB-vs-upload reliability gap.
2. rw_reset_fatigue_nonrenewal displayed reproducible immediate-failure behavior (0 elapsed, empty stderr signatures across hosts), justifying queue halt to prevent wasted volunteer compute.
3. The active research line now emphasizes mechanism discrimination for weight-decay timing effects via batch-noise interaction (CPU) and curvature/timing trigger tests (GPU).

NOTES
- This session log intentionally omits cumulative credited result ID lists; database state remains source of truth.