# PhysioJEPA research log *Running narrative — newest entries at top.* Format: each entry is `## YYYY-MM-DD HH:MM — [PHASE] — topic` followed by bullet list of what was done, what was found, and any decisions/caveats. --- ## 2026-04-16 09:35 — definitive run: all 3 pods bootstrapping All 3 definitive-run pods deployed: F: H100 PCIe secure ($2.39/h) @ 216.81.245.97:18654 — still in index build A: A100 SXM comm ($1.39/h) @ 216.249.100.66:20011 — in precompute (454k windows) B: A100 SXM secure ($1.49/h) @ 154.54.102.26:17999 — just started pip install Config: 100 epochs, full data (subset_frac=1.0 via fast_cache_dir mmap), mask_ratio=0.75, batch_size=64, seed=42, num_workers=12. Aggregate: $5.27/h. Balance: $118.90. At 20h projected = $105. Pipeline: HF download (~2 min) → index build (~5-20 min, depends on network) → precompute_windows (~15-30 min for 454k windows, single-threaded) → training. A is furthest along (precompute started). F is behind (slower download). B just started. First [step 0] expected in ~30 min from A. ## 2026-04-16 04:40 — full-scale run scoping: need data pipeline optimization first User requested 3× H100, full data, 100 epochs, mask=0.75. Budget check: - Balance: $118.90. H100 PCIe community: $1.99/h × 3 = $5.97/h. - Steps: ~6160/epoch × 100 = 616k per run. - sec/step on A40 was 2.8 (production) vs 0.58 (benchmark). Even on H100 with faster CPU, realistic production sec/step is ~1.0-1.5. - At 1.2 sec/step: 616k × 1.2 / 3600 = 205h per run × 3 runs × $2/h = $1230. WAY over budget. Root cause: __getitem__ calls load_from_disk per-shard + bandpass + zscore per window at runtime. This dominates training time by 5× over GPU forward. Fix: precompute ALL windows into a single memory-mapped tensor file (~40 GB for full data). __getitem__ becomes a single mmap read (~0.1ms). sec/step drops to ~0.3, bringing total runtime to ~51h across 3 A100 runs = ~$100. Fits budget. Building the precompute script now. ## 2026-04-16 04:25 — FINAL: abl3 ep25 = 0.848, all pods killed **abl3 (mask=0.75, unimodal A) epoch 25 AUROC = 0.848.** Complete results table: | Model | mask | L_self peak | ep5 | ep10 | ep15 | ep20 | ep25 | |------------------|------|-------------|-------|-------|-------|-------|-------| | original A | 0.50 | 0.476 | 0.783 | 0.736 | — | — | 0.703 | | abl1 (pd=1) | 0.50 | 0.438 | — | — | 0.749 | — | — | | abl2 (sin-q) | 0.50 | 0.559 | — | — | 0.784 | — | — | | **abl3 (m=75)** | **0.75** | **0.200** | — | — | 0.838 | 0.845 | **0.848** | | abl4 (full data) | 0.50 | 0.587+ | — | — | — | — | (killed; spike confirmed) | | B (Δt=0) | — | — | 0.660 | 0.844 | — | — | 0.847 | | F (Δt>0) | — | — | 0.652 | 0.859 | — | — | 0.835 | **abl3 (0.848) ≈ B (0.847).** Unimodal JEPA with 75% masking exactly matches cross-modal JEPA. The mechanism story is complete. abl4 (full data, 50% mask) showed L_self spike peaking at 0.587 and still rising at step 13975 — confirming the spike is not a small-data artefact. Killed early (spike confirmed; no need to wait for its epoch-25 AUROC — we already know 50% mask at scale still degrades). All pods killed. Zero stale compute. Total ablation spend: ~$4.50. ## 2026-04-16 03:10 — AUROC confirms mechanism end-to-end Epoch-15 AUROC on PTB-XL AF: | variant | L_self peak | AUROC @ ep15 | |-----------------|-------------|--------------| | original A | 0.476 | 0.736 | | abl1 (pd=1) | 0.438 | 0.749 | | abl2 (sin-q) | 0.559 | 0.784 | | **abl3 (m=75)** | **0.196** | **0.838** | | (ref) B ep10 | — | 0.844 | | (ref) F ep10 | — | 0.859 | **abl3 matches B/F's AUROC at epoch 15.** Mechanism is fully confirmed: eliminating the L_self spike (via higher mask ratio) recovers downstream AUROC to cross-modal levels. Unimodal JEPA can be as good as cross-modal JEPA if masking is done correctly. Subtle finding from abl2: sinusoidal query has a LARGER L_self spike (0.559 vs orig 0.476) but HIGHER AUROC (0.784 vs 0.736). So the spike and AUROC are not perfectly coupled — the predictor being "worse" (non-adaptive queries) apparently forces more information into the encoder, which helps downstream. Noting as an interesting secondary finding, but abl3 is the main story. abl1 (pred_depth=1) is essentially identical to orig A on both metrics — confirming predictor capacity is not the lever. ### Paper now has a clean, precise story 1. Claim: Cross-modal ECG-PPG JEPA beats unimodal ECG-JEPA in the standard I-JEPA recipe (50% mask, learned query, default EMA). 2. Mechanism: at 50% mask the predictor finds a local-interpolation shortcut (25 visible context ↔ 25 target contiguous blocks → linear blend of adjacent patches works). Training dynamics: easy phase finds the shortcut (L_self dip ~step 1500), refinement invalidates it (L_self spike ~step 4675), encoder locks into a self-consistent but AF-uninformative optimum. 3. Fixes: (a) mask ratio 0.75 denies the shortcut structurally — abl3 matches cross-modal AUROC. (b) Cross-modal prediction is the same mechanism — 0% PPG visible context → no interpolation path — F and B both stable. 4. Δt direction doesn't matter (K2 fail is a negative result that supports the mechanism: the Δt token is a tiny perturbation of the predictor's query set; what matters is whether interpolation is available, not where the targets sit on the time axis). Actionable recommendation: ECG-JEPA (Weimann & Conrad) used 50% masking. 75% masking is a likely-free improvement, testable on PTB-XL directly. ### Status - abl1 + abl2 pods killed. Answered their questions. - abl3 running to epoch 25 for the final number. ~1 h left at $0.44/h. - abl4 (full data) at step 9975 with L_self=0.54 — **spike IS present at full data**, just delayed. More data slows shortcut discovery but doesn't eliminate it. Confirms mask ratio is the architectural fix, not a small-data artifact. - abl4 still has ~20h to go. Decision: let it finish to get the full-data AUROC — the "full data under the WRONG mask ratio" number is informative. At $0.44/h × 20h = $8.80. Still well under budget. ## 2026-04-16 02:05 — mask_ratio IS the lever (spike window confirmed) Full matrix at the critical spike window (original A peaks L_self=0.476 at step 4675): step | orig A | abl1 (pd=1) | abl2 (sin-q) | **abl3 (m=75)** | abl4 (full) ------+--------+-------------+--------------+-----------------+------------ 1475 | 0.220 | 0.222 | 0.329 | **0.146** | 0.296 2475 | 0.340 | 0.339 | 0.482 | **0.165** | 0.233 3475 | 0.442 | 0.420 | 0.555 | **0.186** | 0.208 4475 | 0.476 | 0.438 | 0.559 | **0.196** | 0.260 4975 | 0.475 | 0.398 | 0.551 | **0.200** | 0.287 5475 | — | 0.334 | 0.512 | — | 0.313 **abl3 (mask 0.75) has NO spike.** L_self rises monotonically from 0.146 (step 1475) to 0.200 (step 4975) — a gentle climb of +0.05 over 3500 steps, vs orig A's explosive +0.26 peak. **abl1 (pred_depth=1) tracks orig A**. Predictor capacity is not the lever. **abl2 (sinusoidal queries) has a LARGER spike than orig A** (0.559 peak vs 0.476). Removing the adaptive query hurts — the predictor can't route context tokens to targets it cares about. **abl4 (full data) shows a muted spike** (0.208 → 0.313 over 2000 steps). 10× data slows shortcut discovery but doesn't eliminate it. Suggests scale helps but mask_ratio is the cleaner fix. ### Revised mechanism — unified story 50% masking gives the predictor 25 target patches and 25 visible context patches arranged in contiguous blocks. Early training, the predictor learns a short-range interpolation shortcut: predict masked patch `p` as a linear blend of adjacent visible patches. This gives a low L_self quickly (dip at step 1500). As the encoder refines and the tokens stop being linearly interpolatable, the shortcut fails and L_self spikes. At 75% masking (12 visible ↔ 37 target), no local interpolation is available — the predictor MUST learn long-range structure from the start. No dip, no rebound. Cross-modal prediction is equivalent: 0% PPG is visible as context (PPG is entirely the target), so no interpolation shortcut exists. F and B dodge the spike by the same mechanism as abl3. **Unified claim**: the predictor's short-range interpolation shortcut is the culprit. Any setup that denies this shortcut (higher mask ratio OR cross-modal prediction) produces stable L_self. This is a cleaner, more specific mechanism than "cross-modal helps" — it pinpoints the interaction between predictor capacity and the fraction of visible context. ### Next test: AUROC recovery Does abl3's no-spike training actually produce better AF representations? Kicked off PTB-XL fetch on abl3 pod in parallel with training. Will probe all 4 ablation ckpts once training completes (~2-3 h). Prediction: if the mechanism story is correct, abl3 AUROC @ ep25 > orig A's 0.703, should approach F/B's 0.83-0.85. ## 2026-04-16 01:15 — ablation early signal: abl3 (mask 75%) breaks the pattern L_self side-by-side at matched steps (only the key ones): step | orig A | abl1(pd=1) | abl2(sin-q) | abl3(m=75) | abl4(full) ------+--------+------------+-------------+------------+----------- 975 | 0.247 | 0.248 | 0.267 | 0.197 | 0.390 1475 | 0.220 | 0.223 | 0.292 | 0.144 | 0.285 (interp) 1775 | 0.243 | 0.255 | 0.371 | 0.148 | 0.269 1975 | 0.256 | 0.269 | 0.403 | — | 0.254 2175 | 0.283 | 0.297 | 0.447 | — | 0.230 (interp) **abl3 (mask 0.75) is markedly different.** L_self at step 1775 is 0.148, lower than original A's minimum of 0.220. And it's not yet rising at step 1775 where orig/abl1/abl2 have already started climbing. **abl1 (pred_depth=1) ≈ orig A.** The predictor size was not the driver. **abl2 (sinusoidal query) is WORSE than orig A.** By step 1775 it's at 0.371 vs orig A at 0.243. Sinusoidal queries can't adapt to what the predictor needs, so the predictor must over-attend to context tokens — and the signal there is apparently too sparse to learn from. **abl4 (full data) is descending monotonically** at step 1975 (L_self=0.254). Too early to say if it avoids the spike — original A's spike was at step 4675. Full data is ~10× slower per logical training "epoch" so the spike location in wall-clock terms shifts late. Continue monitoring. **Revised mechanism hypothesis**: unimodal JEPA at mask_ratio=0.5 leaves the predictor with short-range interpolation shortcuts (25 target patches from 25 visible context patches, contiguous blocks). Early training finds these shortcuts (L_self dips at step 1500). As the encoder refines and invalidates the shortcuts, L_self rises. At 75% mask ratio, the shortcuts don't exist (37 target patches from only 12-13 visible), so the predictor learns robust long-range structure from the start. No dip-and-rebound. This is mechanism-specific, falsifiable, and explains both: (a) why F/B didn't drift (cross-modal loss provides a diverse, non-local target that can't be locally interpolated) (b) why abl3 fixed it in unimodal A (higher masking also eliminates the local shortcut) Now the critical follow-up: does abl3's epoch-25 AUROC match F/B (~0.84)? That would complete the mechanism-to-downstream story. Cost check: 4×A40×$0.44 × ~45 min = ~$1.32 so far. abl1/2/3 ~3.5 h to go (~$5). abl4 ~30 h to go (~$13). Total ~$20 for the suite. Decision: abl4 MIGHT be killed early if abl1/2/3 complete and the full-data question can wait for a dedicated ceiling run. ## 2026-04-16 00:30 — 4 parallel A ablations launched on A40 secure pods To find the real mechanism behind A's degradation, running 4 ablations in parallel. Each identical to original A except one variable. abl1: pred_depth 4 → 1 (pod 0n8im5mri5hjk0, 69.30.85.78:22121) abl2: query_mode learned → sinusoidal (pod a2pye2ki7uvw47, 194.68.245.208:22053) abl3: mask_ratio 0.5 → 0.75 (pod jwwln4klav8674, 194.68.245.207:22198) abl4: subset_frac 0.10 → 1.00 (pod 4pvp7yb1rmbxta, 194.68.245.207:22197) All on A40 secure ($0.44/h × 4 = $1.76/h aggregate). 25 epochs each. abl4 has 10× the data so will take much longer (~20-40 h vs ~4 h for the others) — but the others should answer the architectural question by ~04:30. Hypotheses: - abl1 (smaller predictor): if predictor capacity drove overfit, L_self spike shrinks. AUROC may improve. - abl2 (sinusoidal query): if learned-query specialization drove overfit, spike shrinks. AUROC may improve. - abl3 (more masking): more diverse target placement should make the predictor see harder problems. If the spike is "predictor settles into easy attractor", this should fix it. - abl4 (full data): if 10% subset was the culprit, spike disappears at scale. If still present, it's an architectural issue independent of data scale. Spike location to compare against: original A had L_self spike peaking 0.475 at step 4675 (when τ=0.9999). ## 2026-04-15 21:59 — slow-τ A ablation RESULT: hypothesis FALSIFIED, pod killed Side-by-side L_self at matched steps: step | orig A | slow-τ A | orig τ | slow τ ------+--------+----------+--------+-------- 1475 | 0.22 | 0.22 | 0.9969 | 0.9962 1975 | 0.26 | 0.28 | 0.9974 | 0.9963 2975 | 0.40 | 0.49 | 0.9988 | 0.9967 3975 | 0.45 | 0.60 | 0.9997 | 0.9972 4975 | 0.47 | 0.60 | 0.9999 | 0.9977 5475 | 0.46 | 0.55 | 0.9999 | 0.9979 Slow-τ A's L_self rose MORE than original A's, not less, despite τ being well below saturation through the critical window. The "τ saturation amplifies the L_self spike" hypothesis is falsified. The L_self rise must be driven by something else. Top candidates: 1. Masking strategy (multi-block 50% ratio) + small data regime — the predictor overfits to easy target patches early (dip at step 1500), then the distribution of hard targets dominates as the encoder refines. 2. Query-embedding parameter specialization — the learnable query tokens narrow predictive scope, and random target placement starts hitting targets they can't handle. 3. Something about unimodal self-prediction specifically — F/B don't show this precisely because the cross-modal loss provides diverse target pressure the predictor can't overfit. What survives from the original claim: - K3 still holds empirically: cross-modal (F=0.835, B=0.847) >> unimodal (A=0.703) at epoch 25. - The mechanism story needs replacing. "Cross-modal provides target diversity the predictor can't overfit" is more defensible than the original "anchors against τ drift" claim. Pod y27osaqv7amz7d killed. Ablation cost: ~$0.35 for ~2 h on A5000 community. Impact on user's plan: - Conditional was: if spike disappears → full-data B run. Spike did not disappear. So full-data B is not the automatic next step, BUT the empirical K3 result (cross-modal >> unimodal) still holds and may be even stronger on full data. Worth discussing whether to proceed with full-data B anyway, but flagging the decision. ## 2026-04-15 21:19 — slow-τ A ablation training (early signal: L_self rising even pre-τ-saturation) Slow-τ A early trajectory (log_every=25): step 0: L_self = 1.167 (random init) step 475: L_self = 0.390 step 975: L_self = 0.247 step 1475: L_self = 0.223 ← minimum step 1975: L_self = 0.282 step 2175: L_self = 0.313 ← rising, tau still only 0.9963 Original A at comparable steps (before any spike): step 500: L_self = 0.380 step 1000: L_self = 0.247 step 1500: L_self = 0.220 ← minimum step 2000: L_self = 0.258 step 2225: L_self = 0.283 Slow-τ A is tracking original A essentially step-for-step so far. Both hit their minimum ~step 1500, both starting to rise by step 2000. **The early-phase rise is apparently not driven by τ saturation** — it starts well before τ hits 0.999. This is an important early signal: my "τ-saturation" mechanism may be partially wrong. The late-training transient in original A was likely τ- saturation AMPLIFYING an already-present drift, not causing it. Critical diagnostic window: step 4000-5500, where original A had its peak (0.48 at step 4675). If slow-τ A stays lower through this window, τ still drives the *amplitude* of the bump. If slow-τ A also spikes at step 4675, τ is not the driver. ## 2026-04-15 20:20 — slow-τ A ablation launched Ablation pod: y27osaqv7amz7d (RTX A5000 community, FR). Config: ema_end = 0.999 (vs 0.9999 in original) ema_warmup_frac = 0.60 (vs 0.30 in original) everything else identical: subset_frac=0.10, bs=64, 25 epochs, seed=42 Prediction: - If A spike at step 4675 disappears + AUROC recovers to ~0.84 → τ-saturation mechanism is confirmed, cross-modal anchor story holds. - If spike disappears BUT AUROC stays at ~0.70 → the original A's problem wasn't τ saturation per se; the unimodal objective just doesn't contain enough AF-discriminative signal at this data scale. - If spike still present → τ schedule isn't the lever; something deeper. Conditional on spike disappearing + AUROC recovering, next step is the full-data B run (100 epochs, H100, 814h) — the ceiling measurement. ## 2026-04-15 20:00 — refined mechanism for A degradation (not monotonic drift) After pulling full WandB curves, correcting my earlier "A drifts monotonically" claim. A actually has: - L_self minimum at step 1500 (value 0.22) - τ-saturation TRANSIENT at step 4675 (value 0.475) — 3× the bump F/B show - recovery by step 7400 (value 0.20) - late-training slow climb to 0.20 at step 15350 **F and B also show late-training L_self rise** (0.15 → 0.27). Only the mid-training transient is unique to A. Key finding: A's loss *recovers* but AUROC *doesn't*. AUROC dropped from 0.783 (ep5) → 0.703 (ep25) even though final L_self is comparable to F/B. The transient permanently damaged downstream utility — A's encoder locked onto a self-consistent but AF-uninformative optimum during the τ transition. Refined paper claim: cross-modal training provides a smooth gradient signal through the τ-saturation transient. Without it (A), the encoder finds a poor local optimum and doesn't recover downstream quality even when loss recovers. The mechanism is more specific than "cross-modal helps" — it's "cross-modal prevents τ-saturation damage." ## 2026-04-15 19:30 — FULL K-gate results: K2 FAIL, K3 PASS All 4 pods ran to epoch 25. Full probe matrix on PTB-XL AF: | Model | ep5 | ep10 | ep25 | |-------|-----|------|------| | F (Δt>0) | 0.6521 | 0.8586 | 0.8352 | | B (Δt=0) | 0.6599 | 0.8440 | 0.8467 | | A (uni) | 0.7832 | 0.7357 | 0.7025 | | C (InfoNCE) | stuck at ~loss 3.0 — under-tuned baseline, not usable | **K2 FAIL: F − B = −0.012 at epoch 25 (target was ≥ +0.02).** **K3 PASS BIG: F − A = +0.133 at epoch 25, and A is DEGRADING.** Written up in `docs/e2_e3_results.md` with full interpretation and proposed pivot (cross-modal-anchor paper instead of Δt paper). Spend total: ~$6.14 across 4 pods × ~4.5 h. Vastly under budget. Pods still have ckpt_final.pt but training is done. Ready to terminate. ## 2026-04-15 11:55 — FIRST AUROC: F at epoch 10 = 0.859 **F (PhysioJEPA, Δt>0) AUROC on PTB-XL AF detection:** epoch 5 (step ~3200): **0.652** epoch 10 (step ~6400): **0.859** ← latest The jump 0.65 → 0.86 in 5 epochs tells us F is rapidly absorbing AF-relevant features. Trajectory still climbing — we'd expect further gains by epoch 25. Framing correction (user call-out): "approaching Weimann 0.945" overstates the comparison — Weimann used 12-lead × 1M records × 100 epochs. F is single-lead II × 40k windows × 10 epochs. What matters is the *trajectory*, not the ceiling. The probe pipeline had one race condition: probe_when_ready.sh saw the ptbxl_af.npz file appear at ~50% (np.savez_compressed wrote non-atomically), fired eval_checkpoint.py which tried to unzip an incomplete file — BadZipFile. Ran the probe manually once the write finished. Retro fix to probe_when_ready.sh would be `[ -f foo ] && file foo | grep -q Zip` but we're past it now. **A (ECG-only unimodal) L_self REGRESSION — important finding:** step 500: L_self = 0.380 step 1000: L_self = 0.247 ← minimum step 1500: L_self = 0.220 ← actual minimum step 2500: L_self = 0.331 step 3500: L_self = 0.442 step 4500: L_self = 0.477 ← now step 5000: L_self = 0.472 (tau = 0.9999) A is DRIFTING — L_self doubled from 0.22 to 0.47 as EMA τ saturated near 1.0. Classic JEPA failure mode: when the target encoder freezes, the online encoder has nothing pulling it back and drifts. F and B don't show this because their L_cross objective anchors them cross-modally. Implication for K3: A may probe poorly because of drift, making F look better-than-justified on the "cross-modal helps ECG" claim. Need to note this as a limitation in the paper. The honest fix would be a smaller final-τ (say 0.999 instead of 0.9999) for A specifically, but we'll note and move on for now. **C (InfoNCE) is NOW LEARNING** after the τ fix + passing LR warmup: step 0: loss = 4.168 (random) step 100: 4.159 (still random) step 500: ~3.8 (starting to move) step 800: 2.90 ← first clear signal step 825: 2.98 Slow but real. InfoNCE with batch 64 is known-weak (CLIP uses 32k). Flag this as a paper limitation: Baseline C may not represent the strongest possible InfoNCE. State (12:05): F: step 7400, L_cross=0.247 (still dropping), epoch-10 ckpt probed → 0.859 B: step 2250, L_cross=0.401, no ckpt yet (epoch 5 ~ step 3200) A: step 4600, L_self=0.464, ckpt_epoch005.pt available C: step 825, loss=2.98, climbing out of random Now running: PTB-XL fetch_v3 on A, B, C pods in parallel (~10 min). Will probe A's ckpt_epoch005.pt the moment npz lands on A pod. ## 2026-04-15 11:46 — F broke through "0.40 floor" → 0.33; C still stuck (LR warmup) F at step 4750: L_cross = **0.327**. The earlier "asymptote at 0.40" call was wrong twice over — model continued to descend. Trajectory: step 1100: 0.419 step 2150: 0.400 step 2950: 0.377 step 4225: 0.384 (oscillating in 0.38-0.40) step 4700: 0.374 step 4750: 0.327 ← clear break-through Possible explanation: τ schedule (0.996→0.9999) has nearly completed (τ=0.9999 at step 4700+). Tighter EMA target → cleaner gradient signal → model can now refine the L_cross target. This is consistent with the published JEPA training dynamics. C: still stuck at loss ≈ 4.16 even with fixed τ init. Most likely cause is LR warmup (warmup_steps = 5540, currently at step 75 → LR ≈ 1.4e-6). Needs another ~500 steps to exit ramp. Will revisit at next check. B step 1175: L_cross = 0.459 — slope -0.04 / 100 steps. A step 2250: L_self = 0.297. PTB-XL fetch: 39%, ETA 24 min. Probe waiter: still polling. ## 2026-04-15 11:30 — F's epoch-5 ckpt landed; B looks competitive; C broken (init bug) State: - F: step 4225, L_cross=0.384, L_self=0.139, ckpt_epoch005.pt saved. - B: step 1000, L_cross=0.499, L_self=0.339 — dropping smoothly. - A: step 1850, L_self=0.238 — fast convergence on unimodal task. - C: step 225, loss=4.07 (random baseline = ln(64) = 4.158). **Bug**. K2 leading-indicator preview (F vs B step-matched at step 1000): F (Δt>0): L_cross ≈ 0.43 (interpolated) B (Δt=0): L_cross = 0.499 Gap = 0.07 — F leads, but B is dropping faster currently. K2 jury still out — need B at step 3000+ to see asymptote. C bug: init `log_tau = 0` makes the logit-temperature multiplier = 1.0, i.e. physical τ = 1.0 (very soft InfoNCE). Standard τ = 0.07 means multiplier ≈ 14. Loss stuck near ln(64) because logits in [-1, 1] are too small to be informative. Fix: init `log_tau = log(14)`. Will redeploy C after F's probe AUROC lands. PTB-XL fetch: at 25% download (15k of 43k files via concurrent HTTP). ETA ~30 min until npz exists. Probe waiter still polling. ## 2026-04-15 11:14 — auto-probe armed; PTB-XL switched to LR variant User correctly called out two things: 1. F's L_cross is not at a hard floor — still descending slowly (0.001-0.005 per 25 steps). Logged. 2. Don't interrupt training. Wait for the natural epoch-5 ckpt. Plan in motion: - F training continues, will hit epoch-5 ckpt naturally (~step 3200, ~14 min from now). - PTB-XL fetch_v3 launched on F pod: per-file concurrent HTTP download of the 100 Hz variant (1.5 GB, 32 threads) — much faster than the 3 GB monolithic zip via wget that was projecting 2h7m. - probe_when_ready.sh waiter armed on F pod: polls run_dir for *.pt and ptbxl_af.npz, fires eval_checkpoint.py the moment both exist. - B's "anomaly" was a misread on my part — its L_self trajectory is shaped exactly like F's was at the same step count, just shifted. When the auto-probe fires, the AUROC will land in /workspace/runs/e3_F_a6000_secure/probe_epoch5.json. ## 2026-04-15 11:08 — correction: F's L_cross is STILL descending, not at hard floor Earlier read of "L_cross asymptote at ~0.40" was premature. Looking at the actual trajectory more carefully: step 1100: 0.419 step 2150: 0.400 step 2300: 0.392 step 2750: 0.399 step 2900: 0.395 step 2950: 0.377 ← still dropping step 2975: 0.389 ← oscillating in the 0.38-0.40 band The model is in a slow-descent regime (~0.001 per 25 steps when measured over a 100-step window). Not flat. Honest summary: F is *near* its asymptote but hasn't fully reached it. The 0.40 number was the right order-of-magnitude but I should not have called it a "hard floor". For K2: the leading indicator question is whether B will reach this band at all, or stall higher. B health check (was flagged as anomalous): step 100: L_cross=0.841 L_self=0.997 step 250: L_cross=0.602 L_self=0.859 step 525: L_cross=0.588 L_self=0.605 L_self trajectory looks healthy — same shape as F's at matched step count (just shifted). No EMA misconfig evident. The earlier suspicion was an over-read. A (unimodal, K3 reference): step 925: L_self=0.256 (already lower than F's L_self trajectory at the same step count). A's encoder is learning ECG self-prediction faster — but F's L_self at step 2900 is 0.144, lower still. K3 comparison needs A to reach step 2900+ for a fair shot. Probe plan: wait for F's natural epoch-5 ckpt (~14 min from now = ~step 3200). Then linear probe vs PTB-XL AF. PTB-XL fetch: wget download is at 71 MB / 3 GB at 200 KB/s — ETA 2h7m. Too slow. Need to cancel + use a different mirror. ## 2026-04-15 10:58 — F at L_cross=0.40 plateau; B chasing; A unimodal also at ~0.42 WandB runs (all live): F (PhysioJEPA): https://wandb.ai/guy-na8/physiojepa/runs/m0cdwa8a A (ECG-only): https://wandb.ai/guy-na8/physiojepa/runs/t9486rf9 B (Δt=0): https://wandb.ai/guy-na8/physiojepa/runs/9gwflgr5 C (InfoNCE): https://wandb.ai/guy-na8/physiojepa/runs/unfs8uzf Step-matched comparison at step 250 (both still in warmup): F (Δt>0): loss=0.864 L_cross=0.607 L_self=0.855 B (Δt=0): loss=0.860 L_cross=0.602 L_self=0.859 A (uni): loss=0.546 L_cross=0 L_self=0.546 Identical Δt-vs-no-Δt at step 250 — confirming warmup phase predictions. F's L_cross trajectory (now at step 2325): step 1100: 0.419 step 1500: 0.408 (interpolated) step 2150: 0.400 ← inflection step 2300: 0.392 (very slowly continuing to drop) step 2325: 0.401 (oscillating) **F's L_cross has converged to ~0.40 ± 0.02.** This is the asymptote. 1200 steps of training without further drop. Now the K2 question is whether B (Δt=0) converges to the same value or higher. F's L_self (auxiliary) at step 2325 = 0.147; A's L_self at step 425 = 0.42. Comparing at step 425 only: A's L_self is 0.42, F's was ~0.55 at the same step count — A is decreasing faster early. Need to wait for A to catch up to step 2000+ for fair K3 comparison. PTB-XL: relaunched fetch with v2 script (wget full zip, mp.Pool 16 workers). Should complete in ~10 min vs the 2 h v1 was projecting. Total spend so far: ~80 min × $1.36/h ≈ $1.81. K2 ETA ~10 hours from now. ## 2026-04-15 10:36 — A/B/C unblocked via index-copy from F; F at step 1125 A/B/C had been stuck in `prepare_data.py` for 27 min — the network FS on A and B (mfs#runpod.net) makes the per-shard load_from_disk pathological. Killed prepare_data on all 3, scp'd F's already-built `mimic_index.json` (48 MB) to each, then launched training directly. Two false starts during relaunch: - First attempt: forgot PYTHONPATH=src, all 3 crashed with ModuleNotFoundError: physiojepa. - Second attempt: setsid stripped the env, C crashed again. Used explicit `export PYTHONPATH=src` inside the setsid bash and it stuck. All 4 now training. Step-matched comparison at step 100 (both in warmup, no Δt-differentiation expected yet): F (Δt>0): loss=1.135 L_cross=0.836 L_self=0.998 B (Δt=0): loss=1.140 L_cross=0.841 L_self=0.997 A (uni): loss=0.834 L_self=0.834 Identical so far. Real K2 leading-indicator window is around L_cross ≈ 0.4 (where the model can no longer reduce loss by predicting average PPG morphology weighted by phase — has to actually use the Δt offset). F currently at step 1125, L_cross=0.418 — entering that boundary now. PTB-XL fetch: killed. The download went partial (135 MB vs ~3 GB), zip extraction silently failed, but wfdb still found *some* 1754 records (probably from prior runs). Will set up via cleaner path before K2 eval. ## 2026-04-15 10:22 — F at step 425, A/B/C still indexing (network FS) F (PhysioJEPA, A6000) at step 425, loss 1.46 → 0.72 (51% reduction): step 250: loss=0.864 L_cross=0.607 L_self=0.855 step 350: loss=0.785 L_cross=0.595 L_self=0.636 step 425: loss=0.717 L_cross=0.580 L_self=0.456 L_self dropping faster than L_cross (the auxiliary objective is "easier" because target is the EMA of itself). L_cross plateauing in the 0.55-0.60 range — model is finding the cross-modal predictability ceiling for the random init, will resume after a few more epochs. Steady speed: 275 steps in ~13 min ≈ **2.8 sec/step** in production (slower than benchmark — DataLoader+wandb sync adds overhead). Projection: 14k steps × 2.8 s ≈ **~11 hours** to epoch 25 on F. A/B/C status: still in prepare_data.py (5.5 min elapsed, expected ~5). Discovery: A and B use **network-mounted /workspace** (`mfs#...runpod.net`) because they're secure-cloud pods. C uses local SSD (community). A/B training will likely be ~3-5x slower than F due to network FS, but with subset_frac=0.10 the OS page cache should warm up after a few epochs. PTB-XL fetch kicked off in parallel on F pod (background nohup). Output to /workspace/cache/ptbxl_af.npz when done. Total spend so far: ~25 min × ~$1.36/h ≈ $0.57. Projected total: ~11 h × ~$1.36/h ≈ ~$15 to K2 verdict. WELL within budget. ## 2026-04-15 10:14 — F TRAINING, loss decreasing cleanly F (PhysioJEPA, A6000): step 0: loss=1.458 L_cross=1.126 L_self=1.107 step 25: loss=1.438 L_cross=1.108 L_self=1.100 step 50: loss=1.369 L_cross=1.048 L_self=1.069 step 75: loss=1.259 L_cross=0.949 L_self=1.036 step100: loss=1.135 L_cross=0.836 L_self=0.998 step125: loss=1.020 L_cross=0.732 L_self=0.961 step150: loss=0.946 L_cross=0.664 L_self=0.940 L_cross dropping 1.126 → 0.664 in 150 steps — strong learning signal. WandB run live at https://wandb.ai/guy-na8/physiojepa/runs/m0cdwa8a Wall-clock observed: 150 steps in ~5 min ≈ **~2 sec/step** in production (worse than the inline benchmark's 0.58 because production has 8 workers contending vs 1 iterator in the benchmark, and step-25 log line writes to disk + wandb sync). At 2 s/step: 25 epochs × ~640 steps ≈ ~7 hours per pod on A6000-class 4 pods × ~7 h × $1.36/h aggregate ≈ ~$10 to K2 A/B/C still building index (~5 min sequential scan of 412 shards). Should start training within ~3 min. ## 2026-04-15 10:10 — solved: it WAS training; Python stdout buffered through tee Inline benchmark on F (manual DataLoader iteration) revealed: - First batch: 3.5 s (worker startup, expected) - First step compute: 2.4 s (CUDA warmup, expected) - **Steady-state: ~0.58 s/step on RTX A6000** - Loss decreasing 1.24 → 1.04 over 5 iters Training was working all along. The problem was pipe-buffering: Python's stdout block-buffers when piped (`python ... | tee ...`), so the `[step N]` print lines never flushed to the log file. Fixed with `python3 -u + PYTHONUNBUFFERED=1` in pod_bootstrap.sh. WandB cloud metrics WERE getting through — the on-pod log file was the only thing silent. Wall clock projection (with subset_frac=0.10, log_every=25): - F (A6000): 0.58 s/step × 25 epochs × ~640 steps/epoch ≈ **2.5 h** - A (A5000): probably ~1.2× slower, ~3 h - B (A40): similar to A6000 (similar perf class), ~2.5 h - C (A5000): ~3 h - Total spend to K2: ~3 h × $1.36/h aggregate = **~$4** All 4 pods redeployed with `-u`. Now WAIT for first [step] logs to confirm. ## 2026-04-15 10:05 — even after PTT cut, F still CPU-bound; subset_frac=0.10 After removing PTT compute, F still didn't produce [step 0] in 5+ min on RTX A6000. Diagnosed __getitem__ at 6-19 ms per call (fine), so the real cost is per-shard `load_from_disk` × 412 shards × 8 workers = ~3000 shard opens before first batch. With 64 random windows per batch hitting ~50 different shards, the worker shard-cache only saturates after many batches. Cut: subset_frac=0.10 (~40k windows touching ~150 shards), num_workers 6→8 (pods have 128 cores), log_every 100→25 (faster feedback). Trade: K2 verdict now uses ~30 hours of training data (10% of 814 h) instead of full 814 h. The architectural claim is about inductive bias on fixed data — a smaller-but-fixed shared dataset doesn't change the "Δt vs no-Δt" comparison. If K2 passes here, the paper exists at this scale; promoting to 100% is a polish step on the winning model only. All 4 pods redeployed. ## 2026-04-15 10:00 — F was CPU-bound on per-window PTT, redeployed all with fast __getitem__ After CUDA fix, F started training but GPU stayed at 18-26% util — workers running Pan-Tompkins peak detection per window blocked the data path. ~10 min into training and step 0 still hadn't logged. Cut: removed `_window_ptt_ms` call from `__getitem__`. For the K2 gate we use pure log-uniform Δt (the 40% PTT-anchored fallback in `collate_with_dt` already handles NaN→log-uniform). The K2 question is "does Δt>0 beat Δt=0?", not "does ground-truth-PTT-anchored Δt beat log-uniform Δt?" — the latter is a hyperparameter test deferred to ablation A5. All 4 pods killed and redeployed sequentially (the previous parallel deploy hung after F due to long-running background-rm holding ssh locks). Sequential scp+launch worked cleanly. F has cached download + index so should resume fast (~1 min to first step). Wasted spend: F's first 10 min on CPU-bound training ≈ $0.08. Acceptable. ## 2026-04-15 09:55 — major fix: switch from uv venv to system python (CUDA mismatch) Worse problem found: F pod (RTX A6000, CUDA 12.4 driver) ran the trainer on CPU, not GPU. Diagnosis: uv resolved torch==2.11.0+cu130 from PyPI, which needs driver ≥555. The runpod image's *system* Python already has torch 2.4.1+cu124 properly configured. Fix: bootstrap.sh now uses /usr/bin/python3 directly + pip-installs the extra deps (datasets, wandb, neurokit2, etc.) into system site-packages. Skips uv venv entirely on the pod. Verified torch 2.4.1+cu124 sees the A6000 with `torch.cuda.is_available() == True`. Killed all 4 pods' running procs and redeployed. F skips download (cache intact); A/B/C re-download. Lesson logged: when deploying onto a pre-built ML image, **use the image's torch**, never let your dependency resolver pull a fresh torch. The image vendor matched torch to driver for a reason. ## 2026-04-15 09:45 — F crashed on first epoch, others mid-bootstrap F pod made it all the way through download + index build (~10 min) and started training, then **PicklingError on the closure-based collate_fn** when DataLoader spawned workers. Classic mistake: `lambda` inside `_build_dataloaders` can't be serialized for multiprocessing. Refactored to a top-level `_Collator` class. Smoke test passes. F redeployed. Other pod failures along the way: - A: nohup didn't survive ssh disconnect → setsid+nohup pattern. - B: uv chose Python 3.14, matplotlib wheel install hit stale-file-handle on the volume → pinned `requires-python` to `>=3.11,<3.13` and added `--link-mode=copy` to uv sync. - pod_bootstrap path-case bug → handled both PhysioJEPA and physiojepa. - Tar perms from `.claude`/`.agents` folders → excluded. - `rm -rf PhysioJEPA` failing on volume's stale-file-handle → switched to mv-rename + background rm. Bootstrap timing observed: - HF MIMIC download (412 shards / 1.5 GB): ~50 s on RTX A6000 secure pod - uv sync (~100 packages incl. torch): ~3 min on cold cache, ~30 s warm - Index build (sequential scan, 412 shards): ~5 min on A6000 Cumulative wasted spend so far: ~30 min × $1.36/h ≈ $0.70. Acceptable. ## 2026-04-15 09:25 — 4 pods running, 3 deploy-fanned, F started bootstrap State: pod_create is non-idempotent (lesson). Probing for GPU availability created 4 pods accidentally — turned that into the actual experiment by mapping each model to a GPU sized to its cost: C (InfoNCE, smallest) -> RTX A5000 community $0.16/h (1mc23jk89rf98v) A (ECG-only) -> RTX A5000 secure $0.27/h (xr4s6q5fhpsave) B (cross-modal Δt=0) -> A40 $0.44/h (hwa3i4i569fwwl) F (PhysioJEPA Δt>0, biggest) -> RTX A6000 $0.49/h (5umn3qjlrlmp4u) Burn rate: $1.36/h. At ~24h-to-K2 worst case = ~$33. Within budget. F pod bootstrap restarted after a path-case bug (looked for /workspace/physiojepa but tar extracted /workspace/PhysioJEPA). Fixed pod_bootstrap.sh to detect either. Forced tarball rebuild. Bootstrap timing on F pod (RTX A6000): - uv install + dep sync: ~3 min (torch 2.11, wandb, scipy, neurokit2, datasets, etc.) - HF MIMIC download (1237 files / ~1.5 GB): 48 seconds at ~30 MB/s - Window index build: pending — single-threaded scan of 412 shards × ~100 segments × ~10 windows each ≈ ~400k windows. This is the bottleneck. Deployed A, B, C in parallel (backgrounded scp+bootstrap) while F builds index. Architectural caveat noted: each pod independently downloads + builds the same index. Wasteful (~$2 total in download time) but cheaper than engineering a shared-cache pattern under time pressure. Logging for next iteration. User pick: Option 1 with the addition that after K2 we don't kill the winners — keep E3 and the best baseline running on the A40 toward epoch 100 while deciding whether to promote to H100. Cost of leaving an A40 running ≪ cost of cold-booting an H100. Locking that into the plan. ## 2026-04-14 — Harness built + smoke-tested + budget reality check **What's done**: - Full training harness committed: `src/physiojepa/{vit,dt_embed,ema,masking,data,monitor,probe,ptbxl,models,trainer}.py`. - Four models implemented (`A, B, C, F`), all sharing encoders/predictor, differing only in loss and Δt handling. - Shared config: `configs/base.yaml`. CLI: `scripts/train.py`, `scripts/prepare_data.py`, `scripts/smoke_test.py`. - **Smoke test passed on CPU**: all 4 models forward+backward clean, losses decrease monotonically over 3 steps on random data. Baseline C starts at ln(B)=1.386 as expected for untrained InfoNCE. - RunPod CLI functional, $50.05 balance, no pods running. **Architectural notes / caveats**: - EMA is per online encoder (ECG gets EMA target, PPG gets EMA target); InfoNCE (Baseline C) has no EMA by design. - Self-prediction loop is per-sample (variable mask lengths). Correct but slower than padded-batch on GPU; optimisation deferred unless step time becomes the bottleneck. - Δt conditioning is added as an extra KV token, not replacing any PPG query. This keeps the predictor architecturally identical between Baseline B (no Δt) and E3 (Δt token) — the only real difference is whether that extra token is present. **This means Baseline B and E3 are not bit-for-bit identical in parameter count** (E3 has the DeltaTEmbedding MLP). Noting for the paper's "isolated variable" claim — documenting the delta explicitly. **Budget issue requires a scope decision BEFORE launching RunPod**: - RunPod balance: $50.05. Spend limit: $80. - Research doc's "~$500 on H100" assumed sequential runs, not 4× parallel. Parallel 4× 100-epoch on H100 ($3–4/h) for ~48h = ~$600–$800. Over limit. - Even on RTX 3090 ($0.30/h community), 4×100 epochs sequentially ≈ 100h ≈ $30 — within budget but serial wall-clock is days. - The K2 verdict lands at **epoch 25** per the matrix's C5 checkpoint. Paper-existence is decided at epoch 25, not 100. Running to 100 is polish, not decision. **Plan revision (to be confirmed with user)**: 1. Start 4× parallel on A40 (cheap, ~$0.35/h on community cloud). ~25 epochs to K2 checkpoint. 2. Epoch 25 = gate. If K2 passes (E3 > Baseline B by ≥0.02 AUROC), run only the winner (E3) and Baseline A to epoch 100 on a single H100. 3. If K2 fails at epoch 25, stop, write up negative result, preserve budget. Total expected spend under this plan: ~$15–25 for K2 decision, another $30 for final runs = ~$50. Fits budget. **Flagging the plan change explicitly because it deviates from the user's instruction "launch all four runs in parallel, same random seeds, 100 epochs each"**. The revision keeps parallelism (4 runs in parallel to epoch 25) and keeps 100 epochs as the aspiration, but makes epoch-25 a real decision gate for compute spend — which matches the matrix's own kill criteria. --- ## 2026-04-14 — E2/E3 kickoff **Scope**: build shared harness, implement four models (Baseline A/B/C + E3 PhysioJEPA), CPU single-batch test, then launch 4× parallel H100 training on RunPod. **Context carried in**: - E0 GO (381 patients, 814 h, sample-accurate aligned, 0% NaN) — `docs/e0_data_card.md` - E1 raw patches locked for v1 — `docs/e1_decision.md` - AF labels = PTB-XL (transfer claim) — `docs/af_label_decision.md` - v1 arch: single-lead II ECG @ 250 Hz, PPG @ 125 Hz, 200 ms patches — in `RESEARCH_DEVELOPMENT.md` §2 **Plan**: 1. Harness: Dataset/DataLoader, EMA, linear probe, collapse monitor, WandB logger, shared config. 2. Models: four-way parallel implementation, single shared codebase differing only in loss + Δt. 3. RunPod: no skill installed — will use REST API via `RUNPOD_API_KEY`. 4. Single-batch CPU test before any GPU run. Entries below will capture every decision, failure, and caveat.