AXIOM BOINC SESSION RESULTS LOG
Session timestamp: 2026-03-03 14:54

PART 1: VALIDATION AND CREDIT
- Reviewed and credited 460 completed exp_* results (outcome mix: 260 success, 200 error/abort) from validation pass `validate_2026-03-03_1452.txt`.
- Session credit awarded: 4,342 total.
- Major credit additions: ChelseaOilman +1,631; WTBroughton +1,284; Steve Dodd +711; _Scandinavian_ +240; marmot +229; PyHelix +95; rilian +105; plus small additions to Vato/amazing/philip-in-hongkong/Vladimir Petrov.
- Top credited experiment families this pass: svdrank (77), wdextwidth (53), wddepth (37), cellular_automata_v2 (25), wdtasksweep (24), plus smaller additions across wd/percolation/depth-width/activation/chaos families.
- Remaining uncredited completed after pass: 85,729 (success: 5,174; failed: 80,555).

STUCK/BROKEN TASK CLEANUP
- Broken active prefixes aborted to avoid wasted volunteer compute:
  - exp_symmetry_breaking_dynamics*: 3
  - exp_loss_landscape_curvature*: 5
  - exp_mode_connectivity_v2*: 4
  - exp_random_label_memorization*: 1
- Dead-host >12h aborts: 0
- Hard-ceiling >48h aborts: 0
- Retirement check in Part 2 found over-seeded families but no new unsent tasks to abort in that pass (ABORT_TOTAL=0).

PART 2: DEPLOYMENT AND EXPERIMENT DESIGN
CPU DEPLOYMENT
- Targeted CPU queue fill pass created 2,937 workunits.
- CPU hosts seen: 81; low-RAM hosts skipped: 2.
- CPU scripts used in that pass: wd_batchnoise_interaction.py, wd_labelsmooth_interaction.py.

GPU DEPLOYMENT
- GPU deployment scripts configured in run pass: wd_curvature_trigger_gpu.py, wd_timing_scale_gpu.py.
- GPU checkpoint from findings memory for this session window:
  - Idle GPU hosts detected at query time: 80
  - GPU workunits deployed: 629 total (313 targeted + 316 untargeted fallback)
  - Primary deployed GPU mechanism script: wd_curvature_trigger_gpu.py
- Operational note: partial target-host materialization persists; untargeted fallback remains the reliable method to avoid idle GPU capacity.

NEW EXPERIMENTS DESIGNED (WITH NOVELTY CHECK DOCUMENTATION)
1. wd_labelsmooth_interaction.py (CPU)
- Hypothesis: label smoothing modulates late-WD inverse critical period effects.
- Novelty check query: "weight decay label smoothing interaction neural networks".
- Prior literature reviewed included label smoothing and decoupled WD work; direct late-vs-early WD timing interaction with label smoothing in one controlled setup was not found as an established result.

2. wd_curvature_trigger_gpu.py (GPU-aware)
- Hypothesis: WD onset should trigger when curvature stabilization emerges, not at fixed epoch only.
- Novelty check query: "adaptive weight decay schedule deep learning".
- Prior adaptive WD papers exist, but curvature-triggered onset as a causal ICP timing mechanism test (vs fixed late onset) appeared unaddressed directly.

KEY SCIENTIFIC FINDINGS
1. March 3 validation replication reinforced existing inverse critical period and WD-timing conclusions; no scientific reversal was found in this review batch.
2. Error-heavy families remained costly; proactive aborting of persistently broken prefixes prevented further volunteer compute waste.
3. Mechanism-testing lines now focus on two high-value questions: (a) WD timing x label smoothing interaction and (b) curvature-triggered WD onset versus fixed late onset.
4. GPU deployment remains capacity-limited by target-host/result-materialization mismatch; untargeted fallback is currently necessary for reliable GPU utilization.

KEY SCIENTIFIC RESULTS STATUS
- Established: inverse critical period behavior for WD across multiple prior lines.
- In progress: interaction/mechanism validation for wd_labelsmooth_interaction and wd_curvature_trigger_gpu.
- Immediate next analysis priority: compare curvature-triggered WD against fixed late WD across completed GPU returns, then test generalization by scale/depth/task.