Axiom BOINC Session Results Log
Session timestamp: March 04, 2026, 14:10 (A10erica/Denver)
Source logs: validate_2026-03-04_1403.txt, run_2026-03-03_1352.log

PART 1: VALIDATION, CREDIT, AND CLEANUP
- Reviewed and credited 40 completed successful experiment results (all with valid experiment_result JSON payloads).
- Credit awarded this validation pass: 754.00 total across 40 results (under 10,000 cap).
- Dominant credited families in this pass: moranmig, chemostat, vicsekdelay_gpu, grayscott_delay_pulse_feedback, plus delay-ecology/control singletons.
- Stuck/broken task cleanup results: dead-host >12h aborts = 0; hard >48h aborts = 0; no nonzero-elapsed uncredited failures at cutoff.
- Broken-experiment screening: no active multi-host deterministic crash pattern found (CPU/GPU split check).
- Website counters after pass: credited_count=1907; total_results_count=1907.

PART 2: DEPLOYMENT AND EXPERIMENT DESIGN
CPU deployment
- Deployment scripts: wd_batchnoise_interaction.py and wd_labelsmooth_interaction.py.
- Queue fill strategy: host-targeted assignment to active CPU hosts with queue deficit, skipping hosts <6GB RAM.
- CPU deployment stats from run log: CPU_HOSTS_SEEN=81, CPU_SKIPPED_LOW_RAM=2, CPU_WU_CREATED=2937.
- Target hosts: active CPU hosts in 72h window selected by queue-deficit rule (per-host list was used internally by deploy script).

GPU deployment checkpoint
- GPU scripts used in deployment pass: wd_curvature_trigger_gpu.py and wd_timing_scale_gpu.py.
- Run log shows GPU pass started but was interrupted before final counters were printed.
- Live checkpoint at save time: 2 active GPU hosts with 4 active GPU workunits total for these scripts (1 curvature-trigger, 3 timing-scale).
- Historical execution footprint for these GPU scripts: 9 distinct GPU hosts observed in result history.

NEW/UPDATED EXPERIMENTS AND NOVELTY CHECK NOTES
- New CPU experiment implemented and deployed: wd_batchnoise_interaction.py.
- Hypothesis tested: late weight decay benefit should interact with gradient-noise regime (small-batch vs large-batch), measured as interaction gain difference.
- Novelty-check documentation captured in run workflow via literature/web queries focused on:
  1) weight decay + label smoothing interaction
  2) adaptive/triggered weight decay scheduling
  3) batch-size and generalization interactions (including decoupled weight decay prior art)
- Rationale: this run targets mechanism-level interaction effects (late-WD benefit dependency on batch-noise regime), not just another fixed WD sweep.

KEY SCIENTIFIC FINDINGS
1. Validation quality remained high: 40/40 reviewed outputs were parseable science-bearing experiment_result payloads, supporting stable cross-host serialization.
2. Runtime profile remained tightly clustered (median ~844s) across dominant families, reinforcing reproducible throughput for ongoing comparative analyses.
3. Delay-ecology/control families and GPU vicsekdelay_gpu continued producing usable outputs without a concurrent multi-host crash signature.
4. The newly added wd_batchnoise_interaction line formalizes a novel mechanism test: whether late-WD gains are contingent on gradient-noise regime rather than merely on elapsed training time.
5. Part 2 GPU checkpoint confirms active WD timing/curvature GPU lines are live (nonzero queued work on active GPU hosts), maintaining continuity of the WD timing mechanistic program.

OPERATIONAL NOTES
- This session log intentionally summarizes by experiment family/type and does not include cumulative result ID inventories.