Axiom BOINC Session Results Log Session: 2026-03-04 03:00 (America/Denver) Part: 3 (Save/Upload) RESULTS REVIEWED AND CREDIT AWARDED (PART 1) - Validation source log: validate_2026-03-04_0254.txt. - Credit awarded in two passes: 174 completed xp_* results, 30.9 total credit (under 10,000 cap). - Per-user awarded: - Steve Dodd: +11.1 - PyHelix: +6.0 - makracz: +5.2 - Orange Kid: +3.2 - kotenok2000: +3.0 - marmot: +2.4 - Post-credit verification: uncredited completed xp_* rows = 0. STUCK/BROKEN TASK CLEANUP (PART 1) - Broken-prefix aborts applied (server_state IN (2,4) -> server_state=5,outcome=5): - kuramoto_delay_noisy_control: 18 - toggle_delay_adaptive_control: 8 - fisher_delay_adaptive_culling: 7 - wildfire_delay_adaptive_suppression: 9 - seir_delay_fatigue_control: 9 - sandpile_delay_adaptive_dissipation: 9 - lorenz96_delay_assimilation_regime_shift: 49 - tritrophic_delay_harvest_resilience: 49 - heavytail_blockclip_spectrum: 11 - Total broken-prefix aborts: 169 - Stuck-task aborts: - Dead-host >12h rule: 0 - >48h hard ceiling: 0 DEPLOYMENT SUMMARY (PART 2) - Run log source: run_2026-03-03_1352.log. - CPU deployment pass: - Script set: wd_batchnoise_interaction.py, wd_labelsmooth_interaction.py. - Runtime output: CPU_HOSTS_SEEN=81, CPU_SKIPPED_LOW_RAM=2, CPU_WU_CREATED=2937. - Persisted WUs currently attributable to this run window (since 2026-03-03 13:52): 6 total on host 123 (Dads-PC) across those two scripts. - GPU deployment checkpoint: - Script set: wd_curvature_trigger_gpu.py, wd_timing_scale_gpu.py. - Workunits created in run window (since 2026-03-03 13:52): 1,035 total (518 curvature-trigger + 517 timing-scale). - Active GPU host count targeted: 85 distinct hosts. - GPU host IDs targeted: 1,6,7,9,15,16,23,29,31,57,67,71,72,74,80,85,86,87,95,105,107,113,115,116,118,123,126,127,137,140,159,192,205,206,209,212,216,217,219,222,223,249,251,253,255,258,267,287,299,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,345,346,347,349,350,351,352,353,354,355,356. NEW EXPERIMENT DESIGN + NOVELTY CHECK DOCUMENTATION (PART 2) - New experiment script designed and uploaded: wd_batchnoise_interaction.py. - Purpose: test whether late weight-decay benefit is larger under small-batch (higher gradient noise) training than large-batch training using ID/OOD generalization gap interaction. - Script validation: server-side python3 -m py_compile succeeded. - Novelty-check evidence logged in Part 2 run: - arXiv/domain queries executed for weight-decay scheduling + batch-size interaction. - reference sweep included classic decoupled WD anchor (rXiv:1711.05101) and targeted interaction/scheduling searches. - decision recorded to proceed with an interaction-focused experiment rather than settled baseline replication. - Retirement gate pass executed before deployment: - Retirement candidates reviewed (e.g., svdrank/wdoptim/etc.), but ABORT_TOTAL=0 in this Part 2 pass. KEY SCIENTIFIC FINDINGS 1. New validated GPU WD payloads (wd_curvature_trigger_gpu, wd_timing_scale_gpu) again produced valid xperiment_result JSON, supporting pipeline integrity for successful GPU WD tasks. 2. Current dominant failure signature in high-failure ecological-delay prefixes remains immediate zero-runtime completion with missing upload payloads, reinforcing dispatch/runtime-path instability as the likely bottleneck. 3. Grayscott-delay behavior remains mixed quality (both valid science payloads and explicit error payloads), unlike several delay prefixes that show near-total immediate failure. 4. Part 2 extended WD mechanism testing with a new batch-noise interaction design to test whether late-WD gains depend on stochastic gradient noise regime. KEY SCIENTIFIC RESULTS (FROM FINDINGS MEMORY CONTEXT) 1. Inverse critical-period WD timing signal remains established with no observed reversal in recent sessions. 2. Active scientific emphasis has shifted toward ecological-delay dynamics plus WD interaction mechanisms (label smoothing, curvature gating, batch-noise interaction). NOTES - This session log intentionally excludes cumulative credited result-ID inventories; credit state is tracked in DB. - Findings context source consulted: indings_summary.txt.