# PROGRESS.md — WP31 PRISME phase log

| date | phase | status | commands / evidence | measurements | gate | blockers / next |
|---|---|---|---|---|---|---|
| 2026-10-29 | 0 env audit | done | `scripts/environment_audit.py --smoke` → `hardware/device_inventory.json`; `scripts/cluster_audit.sh` (read-only SSH) | laptop M5 Max 18c/40 GPU/48 GB, torch 2.15 MPS+CPU smoke OK; 12 LAN nodes reachable incl. 2× M3 Ultra 96 GB, M2 Ultra 64 GB, 3× M4 Max 64 GB | PASS | — |
| 2026-10-29 | 1 literature | done | `literature/papers.bib` (77 verified entries), `comparison_matrix.csv`, `novelty_audit.md` | all 12 mandated refs exist incl. RecurTrace 2609.03379 and ELR 2610.12582; C3 (differential zero-init bypass) judged not novel; C2 novel only in its multi-action+propensity form | PASS | preregistration frozen before any test split is read (see protocol/) |
| 2026-10-29 | 2 backbone + counts | done | `scripts/count_params.py --all-configs --device meta`; `scripts/estimate_flops.py` | backbone counts match brief exactly (99,502,848 / 446,744,064 / 1,149,339,648); workspace 2,631,173 vs brief 3,088,260 (reconciled, D3); ops table reproduces brief within 1.3 % | PASS | — |
| 2026-10-29 | 3 invariants | done | `python -m pytest tests -q` → 22 passed | 15 mandated invariants + controller target-freedom + dense ablation + 5 cache-equivalence tests | PASS | — |
| 2026-10-29 | 4 learnability (backbone) | done | `p4_tiny_backbone.yaml` → run `p4_tiny_backbone_seed01_20261029232626` (2.1M params, 6k steps, char-level) | exact match (greedy): dev_iid arith 16.8 %, dev_template 13.7 %, dev_depth 3.0 %, compose 2.4 % (first-600-item subsets, arith-heavy) | PASS (weak backbone, usable comparator) | stronger v2 backbone (5.6M, 15k steps) running |
| 2026-10-29 | 4 learnability (overfit) | done | `p4_overfit64_b8.yaml` → run `p4_overfit64_b8_seed01_20261029235111` | 64 items, budget 8, frozen backbone: train loss 0.347 → 0.0024 in 200 steps; dev loss rises 0.73 → 1.36 (expected) | PASS (gradient path + capacity) | — |
| 2026-10-29 | 4 learnability (generalisation, v1 backbone) | done | zero-U scaled (laptop), U std 0.2 / 0.5 (M4M64c / M2U64), budget-8-only w64/w128 and mixed w128 lr 3e-3 (M2U64 / M4M64c / M4M64a) | after 2000 steps (U std 0.2): dev-iid loss −1.4 % at budgets 1–2, +1.7 % at budget 8; exact match dev-iid 43.8 → 45.8 % (b2), template/depth unchanged, compose 2.3 → 3.0 %; zero-U + scale alone: −0.6 %; lr 3e-3 w128: collapse to inert edits; global-recurrence adapter (same recipe): loss ≥ backbone at every budget, EM 43.8 → 45.8 % (b8) | PASS (weak, learnable; mechanism effect ≈ 1 %) | decisive 5-seed matrix on v2 backbone |
| 2026-10-30 | 4 decisive matrix (v2 backbone, 5 seeds × {PRISME, global recurrence, dense}) | running | `scripts/launch_seed_matrix.sh` → 15 runs on M2U64/M4M64c/M4M64a/M5M36/M4BP48/M4BP36 | — | — | aggregate with `scripts/make_tables.py` |
| 2026-10-30 | 5 counterfactual labels + oracle audit (tiny) | done | `labels_p4.yaml` → run `labels_p4_seed01_20261030000543` (80,632 train + 20,040 calib labels; 4,778 + 1,308 exhaustive-audit states); `evaluation.label_analysis` | gains: mean 0.004, 42 % positive, 37 % harmful; variance 86–89 % between states vs 11–14 % within state (action identity); 37 % of states have all 24 actions positive, 29 % all negative; one-step oracle 0.0166 nats/state = 0.0098 (perfect stop, random action) + 0.0067 (action selection); random action 0.0004 | PASS (oracle margin exists; action choice is ≈40 % of it) | controller fit |
| 2026-10-30 | 6 controller (tiny) | partial | `controller_p4.yaml` → `controller_p4_seed01_20261030000716` (z features): calib sign acc 49 %, captured fraction −3 %; sweep: best (λ=0.1, κ=0.5) loss −0.1 % with 0.01 edits/token; `controller_p4_hent.yaml` (h + backbone entropy): sign acc 59–60 % at 1–2k steps then overfits (early stopping added) | controller with z-only features = chance; h+entropy features predict the sign above chance but capture ≤ 5 % of the oracle opportunity | OPEN (H3 null so far at tiny scale) | rerun with early stopping; more labels |
| 2026-10-30 | 7 inference engine + latency (tiny) | done | `tests/integration/test_cache_equivalence.py` (5 tests pass); `evaluation.latency` on the laptop MPS → `results/raw/latency_p4_*_mps.json` | batch-1 decode: 2.8 ms/token (budget 0) → 5.3 (1 edit) → 19.3 ms (8 edits); dense 8 edits 17.3 ms (not slower than sparse at this scale); controller call ≈ +2.9 ms even with 0 edits | PASS (equivalence); dispatch overhead dominates at tiny scale (brief §13.6 confirmed) | re-measure at 103M |
| 2026-10-29 | 8 pilot (Stage A) | running | M3U96a: `pretrain_103m.yaml` (run `pretrain103m_seed01_*`); M3U96b: `pretrain_enlarged_102m.yaml` | 14.8k tok/s, 196.6M tokens planned (~3.7 h each) | — | editor/controller stages after completion |