Buckets:
| #!/usr/bin/env python | |
| """Row-by-row head-to-head of our AIDE results against the THREE tables of the Driver-WM paper (arXiv 2605.05092v2). | |
| Outputs (new files only): aide/results/tables/driverwm_tables_headtohead.md and .json; appends ONE row to docs/decisions.md. | |
| Read-only on aide/results/runs. No training is launched; the runs that would complete the mirror are listed with their cost. | |
| Sources, in order of preference: | |
| * our geometry / semantic LEVELS (mean ± sd over seeds): `aide/results/tables/master_aide.json` (report_master_aide.py; every | |
| value recomputed from pred_test.npz and cross-checked against metrics_test.json, 0 mismatches) — nothing is retyped; | |
| * the 95 % CI of a group level and every PAIRED Δ: recomputed here from `pred_test.npz` with `aide/eval/metrics.py` on the | |
| `load_split("test")` ground truth (clip bootstrap, 10k resamples, seed 0; F1: 2k joint resamples), because master_aide.json | |
| carries no CIs; per-clip errors are averaged over the group's seeds before resampling (= CI of the seed-mean level); | |
| * runs that master_aide.json does not group (kp+sem no-vision (ii), semantic-only, vis_pooled, recursive rollout): computed | |
| here from pred_test.npz / metrics_test.json with the same functions; | |
| * interventions: `aide/results/tables/run_interventions_latest.json` (aide/eval/interventions.py) with every per-run | |
| `<run>__do_<x>/metrics_test.json` re-read and compared to the summary (tolerance 1e-6); | |
| * published numbers: typed by the owner in the brief (OWNER_TYPED_* below) and cross-checked against | |
| `aide/eval/paper_rows.py`; any discrepancy is reported in §0. | |
| """ | |
| from __future__ import annotations | |
| import argparse | |
| import json | |
| import os | |
| import sys | |
| import time | |
| from pathlib import Path | |
| import numpy as np | |
| ROOT = Path(__file__).resolve().parents[2] | |
| sys.path.insert(0, str(ROOT)) | |
| from aide.data import protocol as P # noqa: E402 | |
| from aide.eval import metrics as M # noqa: E402 | |
| from aide.eval.harness import align, load_split # noqa: E402 | |
| from aide.eval.paper_rows import COLS, LOWER_IS_BETTER, NON_CAUSAL, PAPER_ROWS, PAPER_T3_FULL # noqa: E402 | |
| RUNS = ROOT / "aide/results/runs" | |
| TABLES = ROOT / "aide/results/tables" | |
| MASTER = TABLES / "master_aide.json" | |
| INTERV = TABLES / "run_interventions_latest.json" | |
| OUT_MD = TABLES / "driverwm_tables_headtohead.md" | |
| OUT_JSON = TABLES / "driverwm_tables_headtohead.json" | |
| DECISIONS = ROOT / "docs/decisions.md" | |
| GEO = ("mpjpe_all", "dn_mpjpe", "mpjpe_hm", "dn_mpjpe_hm", "pck05") | |
| F1S = ("f1_dbr", "f1_der", "f1_tcr", "f1_vcr") | |
| COLNAME = {"mpjpe_all": "All-MPJPE", "dn_mpjpe": "d-nMPJPE", "mpjpe_hm": "HM-MPJPE", "dn_mpjpe_hm": "HM d-nMPJPE", | |
| "pck05": "PCK@0.05", "f1_dbr": "F1 DBR", "f1_der": "F1 DER", "f1_tcr": "F1 TCR", "f1_vcr": "F1 VCR"} | |
| ND = {"mpjpe_all": 2, "dn_mpjpe": 3, "mpjpe_hm": 2, "dn_mpjpe_hm": 3, "pck05": 2, "f1_dbr": 2, "f1_der": 2, "f1_tcr": 2, "f1_vcr": 2} | |
| PUB_ND = {"mpjpe_all": 2, "dn_mpjpe": 2, "mpjpe_hm": 2, "dn_mpjpe_hm": 2, "pck05": 2, "f1_dbr": 2, "f1_der": 2, "f1_tcr": 2, "f1_vcr": 2} | |
| # ----------------------------------------------------------------------------------------------- owner-typed tables (brief) | |
| # Table 1 (main results, 5→5 rollout): All / d-n / HM / HM d-n / PCK@0.05 / F1 DBR / DER / TCR / VCR | |
| OWNER_TYPED_T1 = [ | |
| ("Zero-Velocity", "motion-only / offline baseline", (52.89, 2.40, 139.19, 6.32, 85.95, 8.27, 14.94, None, None)), | |
| ("ST-GCN", "motion-only / offline baseline", (110.98, 5.04, 158.10, 7.18, 60.97, 18.96, 22.66, None, None)), | |
| ("SiMLPe", "motion-only / offline baseline", (106.38, 4.83, 156.29, 7.09, 63.45, 37.26, 41.69, None, None)), | |
| ("MotionBERT", "motion-only / offline baseline", (73.51, 3.34, 141.53, 6.42, 78.01, 56.70, 52.71, None, None)), | |
| ("Offline Enc-Dec", "motion-only / offline baseline", (75.87, 3.45, 144.05, 6.54, 77.17, 53.82, 50.68, None, None)), | |
| ("Static pooling", "controlled contextual baseline (identical VLM interface; no rollout)", (68.50, 3.11, 134.56, 6.11, 72.55, 54.04, 60.07, 26.39, 14.00)), | |
| ("Single-stream", "controlled contextual baseline (identical VLM interface)", (90.87, 4.13, 147.44, 6.69, 59.63, 24.56, 24.35, 26.39, 14.00)), | |
| ("Late fusion", "controlled contextual baseline (identical VLM interface)", (82.59, 3.75, 143.31, 6.51, 65.07, 45.87, 49.10, 87.85, 65.97)), | |
| ("Cross-Attn only", "controlled contextual baseline (identical VLM interface)", (80.14, 3.64, 142.41, 6.47, 66.43, 62.52, 68.75, 87.94, 69.09)), | |
| ("Driver-WM main", "theirs (main)", (71.47, 3.24, 138.03, 6.27, 71.66, 68.07, 72.61, 90.15, 68.34)), | |
| ("full pretrained", "theirs (Driver-WM, full pretrained)", (72.68, 3.30, 138.03, 6.27, 70.99, 73.22, 73.97, 90.54, 62.09)), | |
| ("Non-causal", "theirs (Non-causal, bidir; reads future frames)", (72.15, 3.27, 136.50, 6.20, 71.39, 73.35, 74.46, 88.65, 68.82)), | |
| ] | |
| # Table 2 (ablations) | |
| OWNER_TYPED_T2 = [ | |
| ("Driver-WM main", (71.47, 3.24, 138.03, 6.27, 71.66, 68.07, 72.61, 90.15, 68.34)), | |
| ("w/o ext context", (71.74, 3.26, 136.94, 6.21, 71.13, 71.33, 63.56, 26.39, 14.00)), | |
| ("RSSM/GRU", (78.42, 3.56, 136.46, 6.19, 67.48, 67.15, 72.29, 88.26, 63.74)), | |
| ("+Skeleton-Pre", (70.92, 3.22, 136.04, 6.18, 72.09, 69.55, 75.65, 88.33, 70.24)), | |
| ("KL bottleneck", (70.56, 3.20, 136.50, 6.20, 71.71, 69.89, 71.54, 88.84, 64.68)), | |
| ("no-prompt feat", (76.54, 3.47, 142.08, 6.45, 68.28, 68.88, 69.92, 76.87, 58.59)), | |
| ("no pose head", (None, None, None, None, None, 67.22, 70.92, 88.83, 69.44)), | |
| ] | |
| T2_LABEL = {"RSSM/GRU": "RSSM/GRU dyn.", "no-prompt feat": "no-prompt feat."} | |
| # Table 3 (controlled interventions; Δ px between intervened and factual rollouts): ΔAll / ΔHM / Δh=5 / ΔHead / ΔHands | |
| OWNER_TYPED_T3 = [ | |
| ("do(Ext=Swap_clip)", "swap", (5.363, 4.794, 8.042, 5.462, 3.502)), | |
| ("do(Ext=∅)", "remove ext", (12.953, 8.691, 15.208, 7.385, 12.937)), | |
| ("do(Ext=Shift_large)", "shift large", (0.793, 0.892, 1.009, 0.661, 0.018)), | |
| ("do(Ext=DropView)", "drop view", (0.419, 0.100, 0.478, 0.392, 0.265)), | |
| ("do(λ_CA=0)/do(g=0)", "disable pathway", (89.641, 62.232, 116.751, 36.783, 95.666)), | |
| ("do(λ_CA=1)/do(g=1)", "force", (26.733, 11.480, 42.170, 15.861, 26.727)), | |
| ("do(λ_CA=2)", "over-injection", (43.015, 23.959, 52.142, 33.909, 43.695)), | |
| ] | |
| PAPER_MAIN_ALL = 71.47 | |
| PAPER_HM5_MAIN = 155.82 | |
| # ----------------------------------------------------------------------------------------------- formatting | |
| def f(v, nd=2, sign=False): | |
| if v is None or (isinstance(v, float) and np.isnan(v)): | |
| return "–" | |
| return f"{v:+.{nd}f}" if sign else f"{v:.{nd}f}" | |
| def pm(mean, sd, nd=2): | |
| return f(mean, nd) if sd is None else f"{mean:.{nd}f} ± {sd:.{nd}f}" | |
| def ci(lo, hi, nd=2, sign=False): | |
| return f"[{f(lo, nd, sign)}, {f(hi, nd, sign)}]" | |
| def md_table(header, rows): | |
| out = ["| " + " | ".join(header) + " |", "|" + "---|" * len(header)] | |
| out += ["| " + " | ".join(str(c) for c in r) + " |" for r in rows] | |
| return "\n".join(out) | |
| # ----------------------------------------------------------------------------------------------- data access | |
| class Store: | |
| """Aligned predictions / logits per run plus per-clip statistics (cached).""" | |
| def __init__(self): | |
| d = load_split("test") | |
| self.clip_ids = d["clip_id"] | |
| self.N = len(self.clip_ids) | |
| self.gt = d["S10"][:, P.T_OBS:] # [N, 5, 136, 3] — the future block only (as report_master_aide.py) | |
| self.hm, self.hm_thr, _ = M.hm_mask(self.gt) | |
| self.labels = {t: np.asarray(d[f"y_{t}"]) for t in P.TASKS} | |
| self.pck_thr = P.pck_threshold_px(0.05) | |
| assert self.N == P.EXPECTED_SPLIT_SIZES["test"] and int(self.hm.sum()) == P.hm_top(self.N) == 60 | |
| self._cache = {} | |
| def exists(self, run): | |
| return (RUNS / run / "pred_test.npz").exists() | |
| def load(self, run): | |
| if run in self._cache: | |
| return self._cache[run] | |
| z = np.load(RUNS / run / "pred_test.npz", allow_pickle=False) | |
| order = align(z, self.clip_ids) | |
| pred = np.asarray(z["pred"], dtype=np.float64)[order] | |
| logits = {t: np.asarray(z[f"logits_{t}"])[order] for t in P.TASKS if f"logits_{t}" in z.files} | |
| err = M.joint_errors(pred, self.gt) # [N, 5, J] | |
| pc = { | |
| "mpjpe_all": err.mean(axis=(1, 2)), | |
| "dn_mpjpe": 100.0 * err.mean(axis=(1, 2)) / P.DIAG, | |
| "pck05": 100.0 * (err <= self.pck_thr).mean(axis=(1, 2)), | |
| "h5": err[:, -1].mean(axis=1), | |
| } | |
| pc["mpjpe_hm"] = pc["mpjpe_all"][self.hm] | |
| pc["dn_mpjpe_hm"] = pc["dn_mpjpe"][self.hm] | |
| yhat = {t: lg.argmax(-1) for t, lg in logits.items()} | |
| rec = {"mpjpe_all": float(err.mean()), "dn_mpjpe": 100.0 * float(err.mean()) / P.DIAG, | |
| "mpjpe_hm": float(err[self.hm].mean()), "dn_mpjpe_hm": 100.0 * float(err[self.hm].mean()) / P.DIAG, | |
| "pck05": 100.0 * float((err <= self.pck_thr).mean())} | |
| for t, yh in yhat.items(): | |
| rec[f"f1_{t}"] = M.macro_f1(self.labels[t], yh) | |
| self._cache[run] = {"pc": pc, "yhat": yhat, "level": rec} | |
| return self._cache[run] | |
| def harness(self, run): | |
| return json.load(open(RUNS / run / "metrics_test.json"))["metrics"] | |
| def macro_f1_fast(y, yh): | |
| """Macro-F1 (%) over the union of labels present in y or yh, zero_division=0 (== sklearn f1_score(average='macro')).""" | |
| labs = np.union1d(y, yh) | |
| f1s = [] | |
| for c in labs: | |
| tp = np.sum((y == c) & (yh == c)); fp = np.sum((y != c) & (yh == c)); fn = np.sum((y == c) & (yh != c)) | |
| p = tp / (tp + fp) if tp + fp > 0 else 0.0 | |
| r = tp / (tp + fn) if tp + fn > 0 else 0.0 | |
| f1s.append(0.0 if p + r == 0 else 2 * p * r / (p + r)) | |
| return 100.0 * float(np.mean(f1s)) | |
| def group_level_ci(store: Store, runs, n_boot=10000, n_boot_f1=2000, seed=0): | |
| """CI of the SEED-MEAN level: per-clip statistics averaged over the runs, then clip bootstrap (geometry); for F1 a joint | |
| clip resample is scored per seed and averaged over seeds. Returns {col: (lo, hi)} (F1 only if every run has logits).""" | |
| recs = [store.load(r) for r in runs] | |
| out = {} | |
| for col in ("mpjpe_all", "dn_mpjpe", "pck05", "mpjpe_hm", "dn_mpjpe_hm"): | |
| v = np.mean([r["pc"][col] for r in recs], axis=0) | |
| out[col] = M.bootstrap_ci(v, n_boot, seed) | |
| if all(rec["yhat"] for rec in recs): | |
| rng = np.random.default_rng(seed) | |
| idx = rng.integers(0, store.N, size=(n_boot_f1, store.N)) | |
| for t in P.TASKS: | |
| y = store.labels[t] | |
| # sanity: fast macro-F1 equals the sklearn one on the full set for every seed | |
| for rec in recs: | |
| assert abs(macro_f1_fast(y, rec["yhat"][t]) - M.macro_f1(y, rec["yhat"][t])) < 1e-9 | |
| b = np.array([np.mean([macro_f1_fast(y[i], rec["yhat"][t][i]) for rec in recs]) for i in idx]) | |
| out[f"f1_{t}"] = (float(np.quantile(b, 0.025)), float(np.quantile(b, 0.975))) | |
| return out | |
| def paired(store: Store, run_a, run_b, n_boot=10000, seed=0): | |
| """Paired per-clip Δ = A − B for All / HM / PCK / h=5 (negative = A better for errors).""" | |
| a, b = store.load(run_a)["pc"], store.load(run_b)["pc"] | |
| out = {} | |
| for col in ("mpjpe_all", "mpjpe_hm", "pck05", "h5"): | |
| d = M.paired_diff(a[col], b[col], n_boot, seed) | |
| out[col] = {"delta": d["mean"], "ci_lo": d["ci_lo"], "ci_hi": d["ci_hi"], "a": float(a[col].mean()), "b": float(b[col].mean())} | |
| return out | |
| def verdict_from_pairs(deltas, lower_better=True): | |
| """BEATS / WORSE / NOT_WORSE / INCONCLUSIVE rule of baton_pretrain.md (≥ 2/3 seeds CI excl. 0, same sign on all).""" | |
| n = len(deltas) | |
| if n == 0: | |
| return "–" | |
| signs = [np.sign(d["delta"]) for d in deltas] | |
| excl = [(d["ci_lo"] > 0) or (d["ci_hi"] < 0) for d in deltas] | |
| same = len(set(signs)) == 1 | |
| if same and sum(excl) * 3 >= 2 * n and sum(excl) >= min(2, n): | |
| improving = (signs[0] < 0) if lower_better else (signs[0] > 0) | |
| return "BEATS" if improving else "WORSE" | |
| if not any(excl) and all(abs(d["delta"]) < 0.25 for d in deltas): | |
| return "NOT_WORSE" | |
| return "INCONCLUSIVE" | |
| # ----------------------------------------------------------------------------------------------- §0 cross-check | |
| def cross_check(): | |
| problems = [] | |
| for name, _, vals in OWNER_TYPED_T1: | |
| for c, v in zip(COLS, vals): | |
| pv = PAPER_ROWS[name][c] | |
| if (v is None) != (pv is None) or (v is not None and abs(v - pv) > 1e-9): | |
| problems.append(f"Table 1 {name} / {COLNAME[c]}: brief {v} vs paper_rows.py {pv}") | |
| for name, vals in OWNER_TYPED_T2: | |
| for c, v in zip(COLS, vals): | |
| pv = PAPER_ROWS[name][c] | |
| if (v is None) != (pv is None) or (v is not None and abs(v - pv) > 1e-9): | |
| problems.append(f"Table 2 {name} / {COLNAME[c]}: brief {v} vs paper_rows.py {pv}") | |
| for _, key, vals in OWNER_TYPED_T3: | |
| pv = PAPER_T3_FULL[key] | |
| if any(abs(a - b) > 1e-9 for a, b in zip(vals, pv)): | |
| problems.append(f"Table 3 {key}: brief {vals} vs paper_rows.py {pv}") | |
| # the 18 rows of stage_bc.md §1b are generated from paper_rows.py (report_stage_bc.py) — parse them and compare too | |
| sb = (TABLES / "stage_bc.md").read_text().splitlines() | |
| i = next(k for k, l in enumerate(sb) if l.startswith("## 1b.")) | |
| rows = [l for l in sb[i:i + 30] if l.startswith("| ") and not l.startswith("| row") and "---" not in l] | |
| seen = 0 | |
| for l in rows: | |
| cells = [c.strip() for c in l.strip().strip("|").split("|")] | |
| if len(cells) < 11 or cells[0] not in PAPER_ROWS: | |
| continue | |
| seen += 1 | |
| for c, s in zip(COLS, cells[2:11]): | |
| pv = PAPER_ROWS[cells[0]][c] | |
| v = None if s in ("–", "-", "") else float(s) | |
| if (v is None) != (pv is None) or (v is not None and abs(v - pv) > 1e-9): | |
| problems.append(f"stage_bc.md §1b {cells[0]} / {COLNAME[c]}: table {s} vs paper_rows.py {pv}") | |
| return problems, seen | |
| # ----------------------------------------------------------------------------------------------- §1 | |
| OUR_ROWS_1 = [ | |
| # (master_aide.json group key, label, note) | |
| ("zv", "Zero-velocity (ours; copy the last observed pose)", "analytic rule; no parameters; the paper's 52.89 on its own extraction"), | |
| ("K3", "CockpitWM (3 locked seeds 42 / 123 / 456)", "keypoint forecaster; geometry checkpoint (val All-MPJPE); 5.8 M params"), | |
| ("K5", "CockpitWM (5 seeds 42 / 123 / 456 / 789 / 1011)", "appendix seeds trained with the locked recipe verbatim"), | |
| ("K5_pre", "CockpitWM + BATON trunk pretraining (5 seeds)", "trunk-only init from BATON in-cabin pose pretraining (pre-registered replication BEATS on Δ All; adopted)"), | |
| ("KS3", "CockpitWM (+ semantic heads), B2 main recipe (3 seeds) — F1 reading (a)", "trunk-state semantic heads; geometry checkpoint (val All-MPJPE); this recipe has no dual selection, so (a) is its only F1 reading"), | |
| ("KSvg_a", "CockpitWM (+ semantic heads), view-gate attention heads (3 seeds) — reading (a)", "checkpoint selected on val All-MPJPE (geometry); F1 of that checkpoint"), | |
| ("KSvg_b", "CockpitWM (+ semantic heads), view-gate attention heads (3 seeds) — reading (b)", "DUAL SELECTION: checkpoint selected on val mean macro-F1 (`__f1sel`); geometry of that same checkpoint (+1–2 px All)"), | |
| ("DWMkp3", "Driver-WM architecture on CockpitWM tokens (3 seeds)", "the reproduction's exterior stream / Perceiver context / gated injection / ST-GCN decoder with CockpitWM's keypoint part tokens; shared recipe; 85.7 M params"), | |
| ("DWMrepro3", "Driver-WM reproduction (our port of the published architecture; 3 seeds)", "absolute-coordinate regression as published; F1 at the geometry checkpoint (the only checkpoint; no EMA)"), | |
| ("ensA3", "ensemble: logit ensemble of the three reading-(b) view-gate seeds (not a single model)", "mean of the members' saved coordinates and logits; n = 1"), | |
| ] | |
| def pub_rows_with(col): | |
| return [(r, v[col]) for r, v in PAPER_ROWS.items() if v[col] is not None] | |
| def verdict_cell(col, ours): | |
| rows = pub_rows_with(col) | |
| lower = col in LOWER_IS_BETTER | |
| beaten = [r for r, v in rows if (ours < v if lower else ours > v)] | |
| not_beaten = sorted([(r, v) for r, v in rows if r not in beaten], key=lambda x: x[1], reverse=not lower) | |
| word = "lower" if lower else "higher" | |
| best = (min if lower else max)(rows, key=lambda x: x[1]) | |
| if len(beaten) == len(rows): | |
| return f"{word} than every published row ({len(rows)}/{len(rows)}; best published {f(best[1], PUB_ND[col])} {best[0]}; margin {f(ours - best[1], ND[col], True)})" | |
| above = "; ".join(f"{r}{'*' if r in NON_CAUSAL else ''} {f(v, PUB_ND[col])}" for r, v in not_beaten[:4]) | |
| more = f" (+{len(not_beaten) - 4} more)" if len(not_beaten) > 4 else "" | |
| return f"{word} than {len(beaten)}/{len(rows)} published rows; not {word} than: {above}{more}" | |
| def section1(store: Store, master: dict): | |
| groups = master["groups"] | |
| header = ["row", "n", *[COLNAME[c] for c in COLS]] | |
| pub = [] | |
| for name, grp, vals in OWNER_TYPED_T1: | |
| pub.append([f"{name} — {grp}", "1 (single seed)", *[f(v, PUB_ND[c]) for c, v in zip(COLS, vals)]]) | |
| ours_rows, ours_json = [], [] | |
| for key, label, note in OUR_ROWS_1: | |
| g = groups[key] | |
| runs = g["runs"] | |
| cis = group_level_ci(store, runs) | |
| cells = {} | |
| line = [f"**{label}**", str(g["n"])] | |
| for c in COLS: | |
| cell = g["cells"].get(c) | |
| if cell is None or cell.get("mean") is None: | |
| line.append("—") | |
| cells[c] = None | |
| continue | |
| lo, hi = cis.get(c, (float("nan"), float("nan"))) | |
| line.append(f"{pm(cell['mean'], cell['sd'], ND[c])} {ci(lo, hi, ND[c])}") | |
| cells[c] = {"mean": cell["mean"], "sd": cell["sd"], "n": cell["n"], "per_seed": cell["per_seed"], "ci95_level": [lo, hi], | |
| "verdict": verdict_cell(c, cell["mean"]), "vs_driverwm_main": cell["mean"] - PAPER_ROWS["Driver-WM main"][c]} | |
| ours_rows.append(line) | |
| ours_json.append({"key": key, "label": label, "note": note, "runs": runs, "n": g["n"], "selection": g["selection"], | |
| "pretraining": g["pretraining"], "reading": g.get("reading"), "cells": cells}) | |
| # verdict table: rows = our rows, cols = metrics | |
| vhead = ["row", *[COLNAME[c] for c in COLS]] | |
| vrows = [] | |
| for r in ours_json: | |
| vrows.append([r["label"], *[(r["cells"][c]["verdict"] if r["cells"][c] else "—") for c in COLS]]) | |
| # margin paragraph | |
| k3 = groups["K3"]["cells"] | |
| all_pub_all = [(r, v["mpjpe_all"]) for r, v in PAPER_ROWS.items() if v["mpjpe_all"] is not None and r != "Zero-Velocity"] | |
| min_other = min(all_pub_all, key=lambda x: x[1]) | |
| every_ge_zv = all(v >= PAPER_ROWS["Zero-Velocity"]["mpjpe_all"] for _, v in all_pub_all) | |
| margins = {} | |
| for c in GEO: | |
| rows = pub_rows_with(c) | |
| best = (min if c in LOWER_IS_BETTER else max)(rows, key=lambda x: x[1]) | |
| m = k3[c]["mean"] - best[1] | |
| margins[c] = {"best_published": best[1], "best_row": best[0], "ours_K3": k3[c]["mean"], "margin": m, | |
| "margin_pct_of_best": 100.0 * m / best[1], "vs_driverwm_main": k3[c]["mean"] - PAPER_ROWS["Driver-WM main"][c], | |
| "margin_in_seed_sd": (m / k3[c]["sd"]) if k3[c]["sd"] else None} | |
| para = ( | |
| f"Every published row is at or above Zero-Velocity on All-MPJPE ({'verified' if every_ge_zv else 'NOT verified'}: the closest published " | |
| f"model is {min_other[0]} at {min_other[1]:.2f} px, {min_other[1] - PAPER_ROWS['Zero-Velocity']['mpjpe_all']:+.2f} px above Zero-Velocity's 52.89; " | |
| f"Driver-WM main is {PAPER_MAIN_ALL - 52.89:+.2f} px above it) — the paper regresses absolute coordinates and none of its rows recovers the " | |
| f"copy-last-pose accuracy. CockpitWM (3 locked seeds, keypoint-anchored residual forecaster) is on the other side of that line on every geometry column; " | |
| f"its margin over the BEST published row per metric: " | |
| + "; ".join( | |
| f"{COLNAME[c]} {f(margins[c]['ours_K3'], ND[c])} vs {f(margins[c]['best_published'], PUB_ND[c])} ({margins[c]['best_row']}) → " | |
| f"{f(margins[c]['margin'], ND[c], True)} ({margins[c]['margin_pct_of_best']:+.1f} % of the best published value" | |
| + (f", {margins[c]['margin_in_seed_sd']:+.0f} seed-sd" if margins[c]["margin_in_seed_sd"] is not None else "") + ")" | |
| for c in GEO) | |
| + f". Against Driver-WM main itself: All {margins['mpjpe_all']['vs_driverwm_main']:+.2f} px, HM {margins['mpjpe_hm']['vs_driverwm_main']:+.2f} px, " | |
| f"PCK@0.05 {margins['pck05']['vs_driverwm_main']:+.2f} points." | |
| ) | |
| note = ("Published rows are single-seed numbers typed from the paper (its Table 8 gives the main model's 3-seed sd: All 0.3, HM 1.2, F1 1.0–1.8); " | |
| "they were produced on the authors' own keypoint extraction and checkpoint selection and CANNOT be paired with our clips — no paired test " | |
| "against a published row exists, and the verdicts are descriptive comparisons of levels. Our rows: mean ± sd over seeds (ddof 1) from " | |
| "`master_aide.json`, followed by the 95 % clip-bootstrap CI of the seed-mean level (per-clip errors averaged over the seeds, 10k resamples; " | |
| "F1: 2k joint resamples) computed here from `pred_test.npz`. * = the Non-causal row reads future frames. The paper's HM h=5 for Driver-WM main " | |
| f"({PAPER_HM5_MAIN}) is not a Table-1 column and is treated in stage_bc.md (trade-off reading; ±51 px CI at n = 60).") | |
| md = [ | |
| "## Section 1 — their Table 1 (main results, 5→5 rollout; horizon-averaged MPJPE px, d-nMPJPE %, PCK@0.05, macro-F1 DBR / DER / TCR / VCR)", | |
| "", | |
| "### 1.1 Published rows (verbatim, single seed) followed by our rows under the same columns", | |
| "", | |
| md_table(header, pub + [["*Ours — mean ± sd over seeds [95 % clip-bootstrap CI of the seed-mean level]; source of the levels: master_aide.json*"] + [""] * (len(header) - 1)] + ours_rows), | |
| "", | |
| note, | |
| "", | |
| "### 1.2 Verdict per metric (our row vs all published rows that report the column; 'lower' for errors, 'higher' for PCK / F1; * = non-causal row)", | |
| "", | |
| md_table(vhead, vrows), | |
| "", | |
| "### 1.3 Every published row is ≥ zero-velocity on All-MPJPE — margin of CockpitWM over the best published row", | |
| "", | |
| para, | |
| ] | |
| return "\n".join(md), {"published": [{"row": n, "group": g, **{c: v for c, v in zip(COLS, vals)}} for n, g, vals in OWNER_TYPED_T1], | |
| "ours": ours_json, "margins_K3": margins, "every_published_row_ge_zero_velocity_on_All": every_ge_zv, | |
| "closest_published_to_ZV": {"row": min_other[0], "All": min_other[1]}, "note": note} | |
| # ----------------------------------------------------------------------------------------------- §2 | |
| def their_delta(name): | |
| main = PAPER_ROWS["Driver-WM main"] | |
| row = PAPER_ROWS[name] | |
| return {c: (None if row[c] is None or main[c] is None else row[c] - main[c]) for c in COLS} | |
| def fmt_delta_row(d): | |
| return " / ".join(f(d[c], ND[c] if c != "dn_mpjpe" and c != "dn_mpjpe_hm" else 2, True) for c in COLS) | |
| def seed_pairs(prefix_a, prefix_b, seeds): | |
| return [(f"{prefix_a}_s{s}_v2", f"{prefix_b}_s{s}_v2") for s in seeds] | |
| def paired_block(store, pairs, label_a, label_b): | |
| """Per-seed paired Δ (A − B) for All / HM / PCK with verdicts, plus seed means of both sides.""" | |
| res = [] | |
| for a, b in pairs: | |
| if not (store.exists(a) and store.exists(b)): | |
| continue | |
| r = paired(store, a, b) | |
| r["run_a"], r["run_b"] = a, b | |
| res.append(r) | |
| if not res: | |
| return None | |
| out = {"label_a": label_a, "label_b": label_b, "n_pairs": len(res), "pairs": res} | |
| for col, lower in (("mpjpe_all", True), ("mpjpe_hm", True), ("pck05", False), ("h5", True)): | |
| ds = [r[col] for r in res] | |
| out[col] = {"mean_delta": float(np.mean([d["delta"] for d in ds])), | |
| "sd_delta": float(np.std([d["delta"] for d in ds], ddof=1)) if len(ds) > 1 else None, | |
| "verdict": verdict_from_pairs(ds, lower), "per_seed": ds, | |
| "level_a": float(np.mean([d["a"] for d in ds])), "level_b": float(np.mean([d["b"] for d in ds]))} | |
| return out | |
| def f1_block(store, runs_a, runs_b, label_a, label_b, harness_ci=True): | |
| """Seed-mean F1 of A and B (reading noted by the caller) and Δ; per-seed harness CIs of the levels.""" | |
| A = [r for r in runs_a if store.exists(r)] | |
| B = [r for r in runs_b if store.exists(r)] | |
| if not A or not B: | |
| return None | |
| out = {"label_a": label_a, "label_b": label_b, "runs_a": A, "runs_b": B} | |
| for t in P.TASKS: | |
| la = [store.load(r)["level"].get(f"f1_{t}") for r in A] | |
| lb = [store.load(r)["level"].get(f"f1_{t}") for r in B] | |
| if any(v is None for v in la + lb): | |
| out[f"f1_{t}"] = None | |
| continue | |
| out[f"f1_{t}"] = {"a": float(np.mean(la)), "a_sd": float(np.std(la, ddof=1)) if len(la) > 1 else None, | |
| "b": float(np.mean(lb)), "b_sd": float(np.std(lb, ddof=1)) if len(lb) > 1 else None, | |
| "delta": float(np.mean(la) - np.mean(lb)), | |
| "a_ci_per_seed": [[store.harness(r)[f"f1_{t}"]["ci_lo"], store.harness(r)[f"f1_{t}"]["ci_hi"]] for r in A] if harness_ci else None, | |
| "b_ci_per_seed": [[store.harness(r)[f"f1_{t}"]["ci_lo"], store.harness(r)[f"f1_{t}"]["ci_hi"]] for r in B] if harness_ci else None} | |
| return out | |
| def seeds_of(pb): | |
| import re | |
| return [int(m.group(1)) for p in pb["pairs"] for m in [re.search(r"_s(\d+)_v2", p["run_a"])] if m] | |
| def fmt_short(pb, col): | |
| x = pb[col] | |
| if x["sd_delta"] is None: | |
| d = x["per_seed"][0] | |
| return f"{f(x['mean_delta'], 2, True)} {ci(d['ci_lo'], d['ci_hi'], 2, True)}" | |
| return f"{f(x['mean_delta'], 2, True)} ± {x['sd_delta']:.2f}" | |
| def fmt_pb(pb, col): | |
| if pb is None: | |
| return "—" | |
| x = pb[col] | |
| per = "; ".join(f"{f(d['delta'], 2, True)} {ci(d['ci_lo'], d['ci_hi'], 2, True)}" for d in x["per_seed"]) | |
| m = f"{f(x['mean_delta'], 2, True)}" + (f" ± {x['sd_delta']:.2f}" if x["sd_delta"] is not None else "") | |
| return f"{m} ({x['verdict']}; per seed {per})" | |
| def fmt_f1b(fb): | |
| if fb is None: | |
| return "—" | |
| parts = [] | |
| for t in P.TASKS: | |
| x = fb[f"f1_{t}"] | |
| parts.append("–" if x is None else f"{f(x['delta'], 2, True)} ({f(x['a'], 1)} vs {f(x['b'], 1)})") | |
| return " / ".join(parts) | |
| def section2(store: Store, master: dict): | |
| groups = master["groups"] | |
| locked = (42, 123, 456) | |
| five = (42, 123, 456, 789, 1011) | |
| axes = [] | |
| # --- w/o ext context | |
| pb = paired_block(store, seed_pairs("cwm3d_kp", "cwm3d_vis_sem", locked), "CockpitWM (keypoint-only; no exterior input)", "CockpitWM (+ semantic heads), B2 main (exterior Qwen features)") | |
| fb = f1_block(store, [f"cwm3d_kpsem_s{s}_v2" for s in locked], [f"cwm3d_vis_sem_s{s}_v2" for s in locked], | |
| "kp+sem, no vision (ii) — heads on the trunk state without exterior features (reading (a))", "B2 main trunk-state heads with exterior features (reading (a))") | |
| ks_a = groups["KS3"]["cells"]; vg_b = groups["KSvg_b"]["cells"] | |
| axes.append({ | |
| "paper_row": "w/o ext context", "their_delta": their_delta("w/o ext context"), | |
| "our_mapping": "geometry: CockpitWM keypoint-only (`cwm3d_kp_s<k>_v2`) − CockpitWM (+ semantic heads) B2 main (`cwm3d_vis_sem_s<k>_v2`), same seed, paired per clip; " | |
| "semantics: kp+sem no-vision (ii) `cwm3d_kpsem_s<k>_v2` − B2 main, both reading (a); the view-gate attention heads (the model that reaches the published F1) " | |
| "ATTEND OVER the exterior features, so 'without exterior' collapses them to the trunk-state heads = B2 main reading (a)", | |
| "geometry": pb, "f1": fb, | |
| "f1_viewgate_collapse": {t: {"viewgate_b": vg_b[f"f1_{t}"]["mean"], "trunk_state_a": ks_a[f"f1_{t}"]["mean"], "delta": ks_a[f"f1_{t}"]["mean"] - vg_b[f"f1_{t}"]["mean"]} for t in P.TASKS}, | |
| "reading": "Same qualitative picture as theirs, at a different accuracy: removing the exterior context costs us " | |
| f"{f(pb['mpjpe_all']['mean_delta'], 2, True)} px All ({pb['mpjpe_all']['verdict']}) vs their +0.27 px — in both models the exterior features barely move geometry — " | |
| f"while semantics drop hard: their DER −9.05 and TCR / VCR to chance (26.39 / 14.00); ours (trunk-state heads) DBR/DER/TCR/VCR " | |
| + " / ".join(f(fb[f'f1_{t}']['delta'], 1, True) for t in P.TASKS) | |
| + "; for the view-gate heads the exterior attention path is worth " | |
| + " / ".join(f(ks_a[f'f1_{t}']['mean'] - vg_b[f'f1_{t}']['mean'], 1, True) for t in P.TASKS) + " F1 ((a) trunk-state − (b) view-gate).", | |
| "status": "mirrored (geometry paired, 3 seeds; semantics seed means)"}) | |
| # --- RSSM/GRU dyn. | |
| pb = paired_block(store, seed_pairs("cwm3d_recursive", "cwm3d_kp", locked), "CockpitWM `--rollout recursive` (1-step recursive decoding, anchors recomputed each step)", "CockpitWM chunked 5-step decoding (main)") | |
| nrec = pb["n_pairs"] | |
| axes.append({ | |
| "paper_row": "RSSM/GRU", "their_delta": their_delta("RSSM/GRU"), | |
| "our_mapping": f"closest existing run(s): `cwm3d_recursive_s<k>_v2` (recursive one-step rollout; Stage-A ablation recipe) − `cwm3d_kp_s<k>_v2`, same seed, {nrec} seed(s) [{', '.join(str(x) for x in seeds_of(pb))}]; " | |
| "no recurrent latent (GRU / RSSM) dynamics variant of CockpitWM exists on AIDE. The BATON-side RSSM decoder (`results/tables/stage_F_rssm.md`, GPU-baselines E) " | |
| "is a stochastic latent rollout for CAN-onset hazards on a different task and metric set (B3-style FAIL: 2 hazard-NLL cells WORSE, kill rule not triggered; " | |
| "far below the pretrained direct heads) — quoted as a qualitative sibling only, not placed in this table.", | |
| "geometry": pb, "f1": None, | |
| "reading": f"Their recurrent dynamics cost +6.95 px All (−1.57 HM); our recursive decoding costs {fmt_short(pb, 'mpjpe_all')} px All and " | |
| f"{fmt_short(pb, 'mpjpe_hm')} px HM at ≈ 3.4× the GPU time ({nrec} seed(s); {pb['mpjpe_all']['verdict']} on All" | |
| + (" — neutral, not to be claimed either way" if pb['mpjpe_all']['verdict'] in ("NOT_WORSE", "INCONCLUSIVE") else "") + ").", | |
| "status": f"{'mirrored' if nrec >= 3 else 'partial'} (decoding-order analogue, {nrec} seed(s); no latent-dynamics variant)"}) | |
| # --- +Skeleton-Pre | |
| pbK = paired_block(store, seed_pairs("cwm3d_kp_batonpre_trunkp123", "cwm3d_kp", five), "CockpitWM + BATON trunk (5 seeds)", "CockpitWM seed init (5 seeds)") | |
| pbKS = paired_block(store, seed_pairs("cwm3d_vis_sem_batonpre_trunkp123", "cwm3d_vis_sem", locked), "CockpitWM (+ semantic heads) B2 main + BATON trunk", "B2 main seed init") | |
| pbVG = paired_block(store, seed_pairs("cwm3d_vis_sema_vg_batonpre_trunkp123", "cwm3d_vis_sema_vg", locked), "view-gate heads (a) + BATON trunk", "view-gate heads (a) seed init") | |
| fbVGa = f1_block(store, [f"cwm3d_vis_sema_vg_batonpre_trunkp123_s{s}_v2" for s in locked], [f"cwm3d_vis_sema_vg_s{s}_v2" for s in locked], "view-gate heads + BATON trunk, reading (a)", "view-gate heads seed init, reading (a)") | |
| fbVGb = f1_block(store, [f"cwm3d_vis_sema_vg_batonpre_trunkp123_s{s}_v2__f1sel" for s in locked], [f"cwm3d_vis_sema_vg_s{s}_v2__f1sel" for s in locked], "view-gate heads + BATON trunk, reading (b)", "view-gate heads seed init, reading (b)") | |
| axes.append({ | |
| "paper_row": "+Skeleton-Pre", "their_delta": their_delta("+Skeleton-Pre"), | |
| "our_mapping": "BATON in-cabin pose pretraining of the trunk (tokenizer / blocks / ln_f), head at seed init (`*_batonpre_trunkp123_*` − seed-init run, same seed): " | |
| "CockpitWM 5 seeds (pre-registered replication, adopted); B2 main 3 seeds (pre-registered, INCONCLUSIVE, not adopted); view-gate heads (a) 3 seeds (pre-registered, adopted); " | |
| "F1 of the pretrained view-gate model under (a) and (b) descriptive (baton_pretrain.md §6–§8)", | |
| "geometry": {"CockpitWM_5seeds": pbK, "B2_main_3seeds": pbKS, "viewgate_a_3seeds": pbVG}, "f1": {"viewgate_reading_a": fbVGa, "viewgate_reading_b": fbVGb}, | |
| "reading": f"Their skeleton pretraining gives −0.55 px All / +3.04 DER / +1.90 VCR / −1.82 TCR (single seed). Ours: CockpitWM {f(pbK['mpjpe_all']['mean_delta'], 2, True)} ± {pbK['mpjpe_all']['sd_delta']:.2f} px All " | |
| f"({pbK['mpjpe_all']['verdict']}, 5 seeds), HM {f(pbK['mpjpe_hm']['mean_delta'], 2, True)} ± {pbK['mpjpe_hm']['sd_delta']:.2f} ({pbK['mpjpe_hm']['verdict']}); " | |
| f"view-gate heads {f(pbVG['mpjpe_all']['mean_delta'], 2, True)} ± {pbVG['mpjpe_all']['sd_delta']:.2f} px All ({pbVG['mpjpe_all']['verdict']}); B2 main " | |
| f"{f(pbKS['mpjpe_all']['mean_delta'], 2, True)} ± {pbKS['mpjpe_all']['sd_delta']:.2f} ({pbKS['mpjpe_all']['verdict']}). Semantics under pretraining move the OTHER way for us: reading (b) " | |
| + " / ".join(f(fbVGb[f'f1_{t}']['delta'], 1, True) for t in P.TASKS) + " (DBR/DER/TCR/VCR) and reading (a) " | |
| + " / ".join(f(fbVGa[f'f1_{t}']['delta'], 1, True) for t in P.TASKS) + " — the pose-only pretraining helps geometry with a paired CI, not the semantic heads.", | |
| "status": "mirrored (paired, 3–5 seeds)"}) | |
| # --- KL bottleneck | |
| axes.append({"paper_row": "KL bottleneck", "their_delta": their_delta("KL bottleneck"), "our_mapping": "none — CockpitWM has no stochastic latent on AIDE (the Laplace uncertainty head is a loss, not a bottleneck); no run to compare", | |
| "geometry": None, "f1": None, "reading": "Their KL bottleneck is −0.91 px All / −1.07 DER (single seed). No mirror; listed under the runs needed.", "status": "none"}) | |
| # --- no-prompt feat. | |
| vp_pairs = [(a_, b_) for a_, b_ in seed_pairs("cwm3d_vis_sem_vp", "cwm3d_vis_sem", locked) if store.exists(a_) and store.exists(b_)] | |
| pb = paired_block(store, vp_pairs, "B2 main with `vis_key=vis_pooled` (vision-token-only pooled Qwen features, no prompt)", "B2 main with prompted `pooled` features") | |
| fb = f1_block(store, [a_ for a_, _ in vp_pairs], [b_ for _, b_ in vp_pairs], "vis_pooled (iv), reading (a)", "B2 main, reading (a)") | |
| nvp = pb["n_pairs"] | |
| axes.append({ | |
| "paper_row": "no-prompt feat", "their_delta": their_delta("no-prompt feat"), | |
| "our_mapping": f"`cwm3d_vis_sem_vp_s<k>_v2` (B2 (iv): `vis_key=vis_pooled`, the vision tokens pooled without the text prompt) − `cwm3d_vis_sem_s<k>_v2`, same seed, {nvp} seed(s) [{', '.join(str(x) for x in seeds_of(pb))}]; F1 reading (a) of both (trunk-state heads)", | |
| "geometry": pb, "f1": fb, | |
| "reading": f"Their no-prompt features cost +5.07 px All and −13.28 TCR; ours {fmt_short(pb, 'mpjpe_all')} px All ({pb['mpjpe_all']['verdict']}) and F1 " + fmt_f1b(fb) | |
| + f" ({nvp} seed(s), trunk-state heads at ≈ 40–80 F1; the view-gate heads were not run on vis_pooled).", | |
| "status": f"{'mirrored' if nvp >= 3 else 'partial'} ({nvp} seed(s); trunk-state heads only)"}) | |
| # --- no pose head | |
| fb = f1_block(store, [f"semonly_s{s}_v2" for s in locked], [f"cwm3d_vis_sema_vg_s{s}_v2__f1sel" for s in locked], | |
| "semantic-only reference `semonly_s<k>_v2` (frozen features → heads, no pose trunk; selected on val F1)", "view-gate heads reading (b) (selected on val F1)") | |
| axes.append({ | |
| "paper_row": "no pose head", "their_delta": their_delta("no pose head"), | |
| "our_mapping": "semantic-only configuration `semonly_s<k>_v2` (frozen Qwen features → the reproduction's heads, no trunk, no pose loss; geometry = zero-velocity copy) − view-gate heads reading (b); both selected on val mean macro-F1", | |
| "geometry": None, "f1": fb, | |
| "reading": f"Their 'no pose head' costs −0.85 / −1.69 / −1.32 / +1.10 F1 (DBR/DER/TCR/VCR); ours " + fmt_f1b(fb) | |
| + " — dropping the pose trunk hurts DER most for us (the driver-emotion head profits from the forecaster state), the other heads within seed noise (sd 0.3–2.9).", | |
| "status": "mirrored (F1 seed means, 3 seeds; geometry undefined for a semantics-only model)"}) | |
| # --- Non-causal | |
| axes.append({"paper_row": "Non-causal", "their_delta": their_delta("Non-causal"), | |
| "our_mapping": "none — CockpitWM's encoder is causal by construction (unit-tested); a bidirectional variant would read future frames and is not a forecasting row", | |
| "geometry": None, "f1": None, "reading": "Their non-causal variant is +0.68 px All / −1.53 HM / +5.28 DBR / +1.85 DER (reads future frames). No mirror by design.", "status": "none (by design)"}) | |
| # markdown | |
| header = ["their ablation", "their Δ vs Driver-WM main (All / d-n / HM / HM d-n / PCK / DBR / DER / TCR / VCR)", "our closest run(s)", "our Δ All [paired, per seed]", "our Δ HM", "our Δ PCK@0.05", "our Δ F1 DBR / DER / TCR / VCR (a − b)", "reading", "status"] | |
| rows = [] | |
| for ax in axes: | |
| g = ax["geometry"] | |
| if isinstance(g, dict) and "pairs" not in g and g is not None: # +Skeleton-Pre: three blocks | |
| dall = "<br>".join(f"{k}: {fmt_pb(v, 'mpjpe_all')}" for k, v in g.items() if v) | |
| dhm = "<br>".join(f"{k}: {fmt_pb(v, 'mpjpe_hm')}" for k, v in g.items() if v) | |
| dpck = "<br>".join(f"{k}: {fmt_pb(v, 'pck05')}" for k, v in g.items() if v) | |
| f1s = "<br>".join(f"{k}: {fmt_f1b(v)}" for k, v in ax["f1"].items() if v) | |
| else: | |
| dall, dhm, dpck = fmt_pb(g, "mpjpe_all"), fmt_pb(g, "mpjpe_hm"), fmt_pb(g, "pck05") | |
| f1s = fmt_f1b(ax["f1"]) if not isinstance(ax["f1"], dict) or "runs_a" in (ax["f1"] or {}) else "—" | |
| rows.append([T2_LABEL.get(ax["paper_row"], ax["paper_row"]), fmt_delta_row(ax["their_delta"]), ax["our_mapping"], dall, dhm, dpck, f1s, ax["reading"], ax["status"]]) | |
| pub_header = ["row", *[COLNAME[c] for c in COLS], "Δ All vs main", "Δ DER vs main"] | |
| pub_rows = [] | |
| for name, vals in OWNER_TYPED_T2: | |
| d = their_delta(name) | |
| pub_rows.append([T2_LABEL.get(name, name), *[f(v, 2) for v in vals], f(d["mpjpe_all"], 2, True) if name != "Driver-WM main" else "—", f(d["f1_der"], 2, True) if name != "Driver-WM main" else "—"]) | |
| md = [ | |
| "## Section 2 — their Table 2 (ablations; single seed) mapped to our closest existing runs", | |
| "", | |
| "### 2.1 Published Table 2 (verbatim)", | |
| "", | |
| md_table(pub_header, pub_rows), | |
| "", | |
| "### 2.2 Axis-by-axis mirror (their Δ = ablation − main; our Δ = ablated − main, paired per clip on the same seed, 10k clip bootstrap; verdict rule as baton_pretrain.md: BEATS / WORSE = CI excl. 0 on ≥ 2/3 seeds with the same sign, NOT_WORSE = all CIs cover 0 and |Δ| < 0.25, else INCONCLUSIVE; single-seed rows carry the CI only)", | |
| "", | |
| md_table(header, rows), | |
| "", | |
| "Selection note: every 'our' level is the test evaluation of the checkpoint selected on the validation split ((a) val All-MPJPE, (b) val mean macro-F1); the F1 Δ are differences of seed means without a paired test (per-seed harness CIs are in the JSON).", | |
| ] | |
| return "\n".join(md), {"published": [{"row": n, **{c: v for c, v in zip(COLS, vals)}, "delta_vs_main": their_delta(n)} for n, vals in OWNER_TYPED_T2], "axes": axes} | |
| # ----------------------------------------------------------------------------------------------- §3 | |
| MAP3 = { # paper key -> (KS = CockpitWM (+ semantic heads) B2 main, gated variant (iii), Driver-WM reproduction) | |
| "swap": ("swap_ext", "swap_ext", "swap_ext"), | |
| "remove ext": ("zero_ext", "zero_ext", "zero_ext"), | |
| "shift large": ("shift_large", "shift_large", "shift_large"), | |
| "drop view": ("drop_view_*", "drop_view_*", "drop_view_*"), | |
| "disable pathway": ("ext_scale0", "gate_override0", "gate0"), | |
| "force": (None, "gate_override1", "gate1"), | |
| "over-injection": ("ext_scale2", "gate_override2", "gate2"), | |
| } | |
| MODELS3_UPDATE_EXTRA = [ | |
| ("KSvg_a", "CockpitWM (+ semantic heads), view-gate heads — reading-(a) checkpoint", [f"cwm3d_vis_sema_vg_s{s}_v2" for s in (42, 123, 456)], 0), | |
| ("KS_pre", "CockpitWM (+ semantic heads) + BATON trunk (B2 main + trunk init)", [f"cwm3d_vis_sem_batonpre_trunkp123_s{s}_v2" for s in (42, 123, 456)], 0), | |
| ] | |
| MODELS3 = [ | |
| ("KS", "CockpitWM (+ semantic heads), B2 main — no traffic gate", [f"cwm3d_vis_sem_s{s}_v2" for s in (42, 123, 456)], 0), | |
| ("KSg", "CockpitWM (+ semantic heads) + traffic gate (iii)", [f"cwm3d_vis_sem_gate_s{s}_v2" for s in (42, 123, 456)], 1), | |
| ("repro", "Driver-WM reproduction (our port)", [f"driverwm_repro_s{s}_v2" for s in (42, 123, 456)], 2), | |
| ] | |
| def latest_per_run(run): | |
| """Newest aide/results/runs/<run>/interventions_2*.json (the harness writes the same record there and in the tables summary).""" | |
| c = sorted((RUNS / run).glob("interventions_2*.json")) | |
| return (json.loads(c[-1].read_text()), str(c[-1].relative_to(ROOT))) if c else (None, None) | |
| def load_interventions_per_run(models): | |
| """{run: record} from the per-run files (newest per run) for every run of `models`; missing runs are absent.""" | |
| S, src = {}, {} | |
| for _, _, runs, _ in models: | |
| for r in runs: | |
| rec, path = latest_per_run(r) | |
| if rec is not None: | |
| S[r], src[r] = rec, path | |
| return S, src | |
| def section3(S=None, models=None, source_note=None, heading_suffix=""): | |
| if S is None: | |
| J = json.load(open(INTERV)) | |
| S = J["summary"] | |
| else: | |
| J = {} | |
| models = models or MODELS3 | |
| models = [m for m in models if any(r in S for r in m[2])] # drop models with no intervention record at all | |
| checks = [] | |
| def iv(run, key): | |
| if key == "drop_view_*": | |
| parts = [S[run]["interventions"][k] for k in ("drop_view_front", "drop_view_left", "drop_view_right")] | |
| dev = {q: float(np.mean([p["deviation_px"][q] for p in parts])) for q in ("all", "hm", "h5", "head", "hands")} | |
| dev["all_max_view"] = float(np.max([p["deviation_px"]["all"] for p in parts])) | |
| gt_all = float(np.mean([p["gt"]["mpjpe_all"] for p in parts])) | |
| gt_hm = float(np.mean([p["gt"]["mpjpe_hm"] for p in parts])) | |
| diff = {"value": float(np.mean([p["diff_vs_factual_all"]["value"] for p in parts])), | |
| "ci_lo": float(np.min([p["diff_vs_factual_all"]["ci_lo"] for p in parts])), "ci_hi": float(np.max([p["diff_vs_factual_all"]["ci_hi"] for p in parts])), | |
| "note": "mean over the three views; CI = envelope"} | |
| return dev, gt_all, gt_hm, diff | |
| p = S[run]["interventions"][key] | |
| # verify from the run output: re-read the intervened run's metrics_test.json | |
| mj = Path(p["metrics_json"]) | |
| if mj.exists(): | |
| m = json.load(open(mj))["metrics"] | |
| ok = abs(m["mpjpe_all"]["value"] - p["gt"]["mpjpe_all"]) < 1e-6 and abs(m["mpjpe_hm"]["value"] - p["gt"]["mpjpe_hm"]) < 1e-6 | |
| checks.append({"run": run, "do": key, "metrics_json": str(mj.relative_to(ROOT)), "match": bool(ok), | |
| "mpjpe_all_json": m["mpjpe_all"]["value"], "mpjpe_all_summary": p["gt"]["mpjpe_all"]}) | |
| else: | |
| checks.append({"run": run, "do": key, "metrics_json": str(mj), "match": None, "note": "file missing"}) | |
| return dict(p["deviation_px"]), p["gt"]["mpjpe_all"], p["gt"]["mpjpe_hm"], dict(p["diff_vs_factual_all"]) | |
| out_rows, out_json = [], [] | |
| header = ["paper intervention", "paper Δ All / HM / h=5 / Head / Hands (px)", "paper Δ/All"] | |
| for _, label, _, _ in models: | |
| header += [f"{label}: Δ All (Δ/All) · HM · h=5 · Head · Hands [n]", f"{label}: All-MPJPE under do(·) vs factual [paired Δ]"] | |
| for pname, key, vals in OWNER_TYPED_T3: | |
| row = [pname, " / ".join(f(v, 3) for v in vals), f"{100 * vals[0] / PAPER_MAIN_ALL:.1f} %"] | |
| rj = {"paper": pname, "key": key, "paper_delta": dict(zip(("all", "hm", "h5", "head", "hands"), vals)), "paper_delta_over_all_pct": 100 * vals[0] / PAPER_MAIN_ALL, "ours": {}} | |
| for mkey, label, runs, idx in models: | |
| ik = MAP3[key][idx] | |
| if ik is None: | |
| row += ["n/a (λ_CA = 1 undefined: no gate)", "—"] | |
| rj["ours"][mkey] = None | |
| continue | |
| per = [] | |
| for r in runs: | |
| if r not in S or (ik != "drop_view_*" and ik not in S[r]["interventions"]): | |
| continue | |
| dev, gt_all, gt_hm, diff = iv(r, ik) | |
| fact = S[r]["factual_gt"]["mpjpe_all"] | |
| per.append({"run": r, "do": ik, "deviation_px": dev, "gt_all_intervened": gt_all, "gt_hm_intervened": gt_hm, "factual_gt_all": fact, | |
| "diff_vs_factual_all": diff, "ci_excl_0": bool(diff["ci_lo"] > 0 or diff["ci_hi"] < 0)}) | |
| if not per: | |
| row += ["not available (no intervention record)", "—"] | |
| rj["ours"][mkey] = None | |
| continue | |
| mean = {q: float(np.mean([p["deviation_px"][q] for p in per])) for q in ("all", "hm", "h5", "head", "hands")} | |
| fact_mean = float(np.mean([p["factual_gt_all"] for p in per])) | |
| gt_mean = float(np.mean([p["gt_all_intervened"] for p in per])) | |
| dmean = float(np.mean([p["diff_vs_factual_all"]["value"] for p in per])) | |
| dsd = float(np.std([p["diff_vs_factual_all"]["value"] for p in per], ddof=1)) if len(per) > 1 else float("nan") | |
| nexcl = sum(p["ci_excl_0"] for p in per) | |
| extra = f" (max view {np.mean([p['deviation_px']['all_max_view'] for p in per]):.2f})" if ik == "drop_view_*" else "" | |
| analog = " ᵃ" if (idx == 0 and ik in ("ext_scale0", "ext_scale2")) else "" | |
| row += [f"{mean['all']:.2f} ({100 * mean['all'] / fact_mean:.1f} %){extra}{analog} · {mean['hm']:.2f} · {mean['h5']:.2f} · {mean['head']:.2f} · {mean['hands']:.2f} [n={len(per)}]", | |
| f"{gt_mean:.2f} vs {fact_mean:.2f} [{dmean:+.2f} ± {dsd:.2f}; CI excl. 0 on {nexcl}/{len(per)} seeds]"] | |
| rj["ours"][mkey] = {"label": label, "do": ik, "n": len(per), "mean_deviation_px": mean, "delta_over_factual_all_pct": 100 * mean["all"] / fact_mean, | |
| "mean_gt_all_intervened": gt_mean, "mean_factual_gt_all": fact_mean, "mean_diff_vs_factual_all": dmean, "sd_diff_vs_factual_all": dsd, | |
| "n_seeds_ci_excl_0": nexcl, "per_seed": per, "analogue": bool(analog)} | |
| out_rows.append(row) | |
| out_json.append(rj) | |
| # ours-only: shift small | |
| row = ["(ours only) do(Ext=Shift_small)", "–", "–"] | |
| rj = {"paper": None, "key": "shift small", "ours": {}} | |
| for mkey, label, runs, idx in models: | |
| per = [] | |
| for r in runs: | |
| if r not in S or "shift_small" not in S[r]["interventions"]: | |
| continue | |
| dev, gt_all, gt_hm, diff = iv(r, "shift_small") | |
| per.append({"run": r, "deviation_px": dev, "gt_all_intervened": gt_all, "factual_gt_all": S[r]["factual_gt"]["mpjpe_all"], "diff_vs_factual_all": diff}) | |
| if not per: | |
| row += ["not available", "—"]; rj["ours"][mkey] = None | |
| continue | |
| mean = {q: float(np.mean([p["deviation_px"][q] for p in per])) for q in ("all", "hm", "h5", "head", "hands")} | |
| fact_mean = float(np.mean([p["factual_gt_all"] for p in per])); gt_mean = float(np.mean([p["gt_all_intervened"] for p in per])) | |
| dmean = float(np.mean([p["diff_vs_factual_all"]["value"] for p in per])) | |
| row += [f"{mean['all']:.2f} ({100 * mean['all'] / fact_mean:.1f} %) · {mean['hm']:.2f} · {mean['h5']:.2f} · {mean['head']:.2f} · {mean['hands']:.2f} [n={len(per)}]", f"{gt_mean:.2f} vs {fact_mean:.2f} [{dmean:+.2f}]"] | |
| rj["ours"][mkey] = {"mean_deviation_px": mean, "mean_gt_all_intervened": gt_mean, "mean_factual_gt_all": fact_mean, "per_seed": per} | |
| out_rows.append(row); out_json.append(rj) | |
| # the sensitivity-without-accuracy point | |
| ks_remove = next(r for r in out_json if r["key"] == "remove ext")["ours"].get("KS") | |
| repro_remove = next(r for r in out_json if r["key"] == "remove ext")["ours"].get("repro") | |
| if ks_remove is None or repro_remove is None: # the update block may be called on a subset of models | |
| ks_remove = ks_remove or {"mean_deviation_px": {"all": float("nan")}, "delta_over_factual_all_pct": float("nan"), "mean_diff_vs_factual_all": float("nan"), "sd_diff_vs_factual_all": float("nan"), "n_seeds_ci_excl_0": 0, "mean_gt_all_intervened": float("nan"), "mean_factual_gt_all": float("nan")} | |
| repro_remove = repro_remove or {"mean_deviation_px": {"all": float("nan")}, "mean_factual_gt_all": float("nan")} | |
| their_acc_change = PAPER_ROWS["w/o ext context"]["mpjpe_all"] - PAPER_MAIN_ALL | |
| point = { | |
| "paper_remove_ext_deviation_px": 12.953, "paper_accuracy_change_without_context_px_table2": their_acc_change, "paper_factual_all": PAPER_MAIN_ALL, | |
| "ours_remove_ext_deviation_px": ks_remove["mean_deviation_px"]["all"], "ours_accuracy_change_under_remove_ext_px": ks_remove["mean_diff_vs_factual_all"], | |
| "ours_factual_all": ks_remove["mean_factual_gt_all"], "ours_all_under_remove_ext": ks_remove["mean_gt_all_intervened"], | |
| "accuracy_advantage_px_factual": PAPER_MAIN_ALL - ks_remove["mean_factual_gt_all"], | |
| "accuracy_advantage_px_under_remove_ext_vs_their_factual": PAPER_MAIN_ALL - ks_remove["mean_gt_all_intervened"], | |
| "repro_remove_ext_deviation_px": repro_remove["mean_deviation_px"]["all"], "repro_factual_all": repro_remove["mean_factual_gt_all"], | |
| } | |
| para = ( | |
| f"Sensitivity without accuracy: the paper's rollout moves by {12.953:.2f} px when the exterior features are removed (Table 3, do(Ext=∅)) while its accuracy without " | |
| f"exterior context changes by {their_acc_change:+.2f} px (Table 2, 71.47 → 71.74) — the deviation is 48× the accuracy change, i.e. the exterior pathway steers the rollout " | |
| f"without making it more accurate. Ours: removing the exterior features moves the CockpitWM (+ semantic heads) rollout by {ks_remove['mean_deviation_px']['all']:.2f} px " | |
| f"(n = 3; {ks_remove['delta_over_factual_all_pct']:.1f} % of its factual All) and costs {ks_remove['mean_diff_vs_factual_all']:+.2f} ± {ks_remove['sd_diff_vs_factual_all']:.2f} px of accuracy " | |
| f"(paired, CI excl. 0 on {ks_remove['n_seeds_ci_excl_0']}/3 seeds): {ks_remove['mean_gt_all_intervened']:.2f} px under the intervention vs {ks_remove['mean_factual_gt_all']:.2f} factual, " | |
| f"i.e. {PAPER_MAIN_ALL - ks_remove['mean_factual_gt_all']:.1f} px more accurate than Driver-WM main's factual rollout and still {PAPER_MAIN_ALL - ks_remove['mean_gt_all_intervened']:.1f} px more accurate " | |
| f"with its exterior input removed. Our port of their architecture reproduces their behaviour in our hands: {repro_remove['mean_deviation_px']['all']:.2f} px deviation at " | |
| f"{repro_remove['mean_factual_all'] if 'mean_factual_all' in repro_remove else repro_remove['mean_factual_gt_all']:.2f} px factual All. Deviations in px are not comparable across models whose " | |
| f"factual error differs by 23 px, hence the Δ/All column; the paper reports single-seed deviations without CIs." | |
| ) | |
| k_note = ("CockpitWM (keypoint-only) has no exterior input: every do(Ext=·) intervention is the identity on its inputs and every Δ is 0 by construction, so it is not a row here " | |
| "(its All-MPJPE 48.63 ± 0.15 is the 'context removed at training time' level of the (+ semantic heads) model; Section 2 'w/o ext context').") | |
| md = [ | |
| f"## Section 3{heading_suffix} — their Table 3 (controlled interventions; Δ px between intervened and factual rollouts) mirrored, with the ACCURACY under each intervention", | |
| "", | |
| "### 3.1 Published Table 3 (verbatim; Driver-WM main, single seed, no CIs)", | |
| "", | |
| md_table(["intervention", "ΔAll", "ΔHM", "Δh=5", "ΔHead", "ΔHands", "ΔAll / 71.47"], [[n, *[f(v, 3) for v in vals], f"{100 * vals[0] / PAPER_MAIN_ALL:.1f} %"] for n, _, vals in OWNER_TYPED_T3]), | |
| "", | |
| "### 3.2 Ours in the same layout (seed means, n = 3; Δ = deviation of the intervened five-step rollout from the factual rollout in px; Δ/All = Δ All ÷ the model's factual All-MPJPE; " | |
| "'All-MPJPE under do(·)' = direct GT error of the intervened rollout, with the paired per-clip difference to the factual rollout and the number of seeds whose 2k-bootstrap CI excludes 0)", | |
| "", | |
| md_table(header, out_rows), | |
| "", | |
| "Operators (aide/eval/interventions.py): swap_ext = exterior features of another test clip (fixed derangement); zero_ext = exterior features zeroed on the observed window; shift_large / small = exterior features delayed 2 / 1 protocol steps " | |
| "(edge-padded); drop_view_* = one exterior view zeroed (row = mean over front / left / right, max view in brackets; the paper's DropView is one number); ext_scale0 / 2 = CockpitWM exterior visual tokens × 0 / × 2 before the trunk " | |
| "(ᵃ analogue of the paper's λ_CA = 0 / 2: the B2 main model has no traffic gate, and λ = 1 is bit-identical to the factual pass); gate_override0/1/2 = the traffic gate of variant (iii) clamped; gate0/1/2 = the reproduction's λ_CA clamped. " | |
| "Head = face + body head joints; Hands = both hands.", | |
| "", | |
| k_note, | |
| "", | |
| "### 3.3 The point", | |
| "", | |
| para, | |
| ] | |
| return "\n".join(md), {"published": [{"intervention": n, "key": k, **dict(zip(("all", "hm", "h5", "head", "hands"), v))} for n, k, v in OWNER_TYPED_T3], | |
| "ours": out_json, "point": point, "run_output_checks": checks, "source": source_note or str(INTERV.relative_to(ROOT)), | |
| "interventions_json_command": J.get("command"), "interventions_json_t_end_utc": J.get("t_end_utc"), "k_note": k_note, | |
| "models": [{"key": m[0], "label": m[1], "runs": m[2]} for m in models]} | |
| # ----------------------------------------------------------------------------------------------- verdict + runs needed | |
| def runs_needed(): | |
| return [ | |
| {"cell": "Table 2 'w/o ext context' for the model that reaches the published F1 (view-gate attention heads)", "run": "not definable: the heads attend over the exterior features; the exterior-free reading is the trunk-state heads (= B2 main reading (a), already in the table)", "cost_gpu_min": 0}, | |
| {"cell": "Table 2 'RSSM/GRU dyn.' — recursive decoding at the other locked seeds", "run": "`cwm3d_recursive_s{123,456}_v2` (K recipe, `--rollout recursive`, 3.4× the K time) and `cwm3d_vis_sem_recursive_s{42,123,456}_v2`", "cost_gpu_min": "≈ 2 × 2.0 + 3 × 7 ≈ 25"}, | |
| {"cell": "Table 2 'RSSM/GRU dyn.' — recurrent latent dynamics", "run": "new decoder option (GRU state over the 5 future steps) on the K recipe, 3 seeds — needs code, not a flag", "cost_gpu_min": "≈ 3 × 0.6–1.0 after implementation"}, | |
| {"cell": "Table 2 'KL bottleneck'", "run": "KL-regularised latent between trunk and horizon head, KS recipe, 3 seeds — needs code", "cost_gpu_min": "≈ 3 × 2–3"}, | |
| {"cell": "Table 2 'no-prompt feat.' at the other locked seeds", "run": "`cwm3d_vis_sem_vp_s{123,456}_v2` (`--vis-key vis_pooled`, KS recipe verbatim)", "cost_gpu_min": "≈ 2 × 2–3"}, | |
| {"cell": "Table 2 'no-prompt feat.' for the view-gate heads", "run": "`cwm3d_vis_sema_vg_vp_s{42,123,456}_v2` (view-gate recipe with `vis_key=vis_pooled`)", "cost_gpu_min": "≈ 3 × 10–13"}, | |
| {"cell": "Table 2 'Non-causal (bidir)'", "run": "none — violates the causal forecasting protocol; not planned", "cost_gpu_min": 0}, | |
| {"cell": "Table 3 for the view-gate heads model (readings (a) and (b))", "run": "`aide/eval/interventions.py --run cwm3d_vis_sema_vg_s{42,123,456}_v2` (evaluation only; (b) needs the `__f1sel` checkpoint, which the run directory does not keep)", "cost_gpu_min": "≈ 1 GPU-min per run (eval only; the 9-run pass took 161 s)"}, | |
| {"cell": "Table 3 for CockpitWM + BATON trunk (+ semantic heads)", "run": "`aide/eval/interventions.py --run cwm3d_vis_sem_batonpre_trunkp123_s{42,123,456}_v2`", "cost_gpu_min": "≈ 1 GPU-min per run (eval only)"}, | |
| ] | |
| def verdict(sec1, sec2, sec3): | |
| ours = {r["key"]: r for r in sec1["ours"]} | |
| k3, vga, vgb, ens, repro, dwmkp = ours["K3"], ours["KSvg_a"], ours["KSvg_b"], ours["ensA3"], ours["DWMrepro3"], ours["DWMkp3"] | |
| geo_all = all("every published row" in k3["cells"][c]["verdict"] for c in GEO) | |
| def n_beaten(row, c): | |
| v = row["cells"][c] | |
| if v is None: | |
| return None | |
| rows = pub_rows_with(c); lower = c in LOWER_IS_BETTER | |
| return sum(1 for _, pv in rows if (v["mean"] < pv if lower else v["mean"] > pv)), len(rows) | |
| def vs_main(row): | |
| return {c: (row["cells"][c]["vs_driverwm_main"] if row["cells"][c] else None) for c in F1S} | |
| def not_beaten(row, c): | |
| v = row["cells"][c]["mean"]; lower = c in LOWER_IS_BETTER | |
| return sorted([(r, pv) for r, pv in pub_rows_with(c) if not (v < pv if lower else v > pv)], key=lambda x: x[1], reverse=not lower) | |
| def best_pub(c): | |
| return (min if c in LOWER_IS_BETTER else max)(pub_rows_with(c), key=lambda x: x[1]) | |
| n_geo_rows = len(pub_rows_with("mpjpe_all")) | |
| t1 = { | |
| "geometry": f"CockpitWM (3 seeds) is {('lower (errors) / higher (PCK) than every published row that reports the column (' + str(n_geo_rows) + ' of the 18 rows; no pose head reports no geometry) on all 5 geometry columns') if geo_all else 'NOT better than every published row on some geometry column'}; " | |
| + "; ".join(f"{COLNAME[c]} {f(sec1['margins_K3'][c]['margin'], ND[c], True)} vs best published ({sec1['margins_K3'][c]['best_row']})" for c in GEO) | |
| + f"; vs Driver-WM main All {sec1['margins_K3']['mpjpe_all']['vs_driverwm_main']:+.2f} px. Driver-WM's architecture on our tokens (Driver-WM-kp) also beats every published row on geometry " | |
| f"(All {dwmkp['cells']['mpjpe_all']['mean']:.2f}) but is WORSE than CockpitWM paired on 3/3 seeds (arch_attribution.md §2: +1.63 / +1.91 / +2.51 px). BETTER.", | |
| "semantics_a": "reading (a), view-gate heads (geometry checkpoint): " + "; ".join(f"{COLNAME[c]} {vga['cells'][c]['mean']:.2f} ({n_beaten(vga, c)[0]}/{n_beaten(vga, c)[1]} published rows below; vs main {vs_main(vga)[c]:+.2f})" for c in F1S) | |
| + " → above Driver-WM main on 3/4 heads (DBR below), above every published row on TCR only. EQUAL-to-BETTER vs main, WORSE than the best published on DBR / DER / VCR.", | |
| "semantics_b": "reading (b), view-gate heads (val-F1 checkpoint, +1.1 px All): " + "; ".join(f"{COLNAME[c]} {vgb['cells'][c]['mean']:.2f} ({n_beaten(vgb, c)[0]}/{n_beaten(vgb, c)[1]}; vs main {vs_main(vgb)[c]:+.2f})" for c in F1S) | |
| + f" → above Driver-WM main on {sum(1 for c in F1S if vs_main(vgb)[c] > 0)}/4 heads by " + " / ".join(f"{vs_main(vgb)[c]:+.2f}" for c in F1S) | |
| + " (their 3-seed sd 1.0–1.8: " + ", ".join(f"{COLNAME[c][3:]} {'outside' if abs(vs_main(vgb)[c]) > 1.8 else 'within'} one sd" for c in F1S) + "); above every published row on " | |
| + (", ".join(COLNAME[c][3:] for c in F1S if not not_beaten(vgb, c)) or "no head") + "; not above: " | |
| + "; ".join(f"{COLNAME[c][3:]} — " + ", ".join(f"{r}{'*' if r in NON_CAUSAL else ''} {pv:.2f}" for r, pv in not_beaten(vgb, c)) for c in F1S if not_beaten(vgb, c)) | |
| + ". BETTER than main, EQUAL to the best published variants (all within their 3-seed sd).", | |
| "ensemble": "the 3-seed logit ensemble (not a single model): " + " / ".join(f"{ens['cells'][c]['mean']:.2f}" for c in F1S) + " — " | |
| + ("above every published row on all four heads (margins over the best published: " + ", ".join(f"{COLNAME[c][3:]} {ens['cells'][c]['mean'] - best_pub(c)[1]:+.2f} vs {best_pub(c)[0]}{'*' if best_pub(c)[0] in NON_CAUSAL else ''}" for c in F1S) + "; the DBR margin is within noise)." | |
| if all(not not_beaten(ens, c) for c in F1S) else | |
| "above every published row on " + (", ".join(COLNAME[c][3:] for c in F1S if not not_beaten(ens, c)) or "no head") + "; not above: " | |
| + "; ".join(f"{COLNAME[c][3:]} — " + ", ".join(f"{r} {pv:.2f}" for r, pv in not_beaten(ens, c)) for c in F1S if not_beaten(ens, c)) + "."), | |
| "reproduction": f"our port of the published architecture lands at All {repro['cells']['mpjpe_all']['mean']:.2f} ± {repro['cells']['mpjpe_all']['sd']:.2f} (their 71.47) with F1 " | |
| + " / ".join(f"{repro['cells'][c]['mean']:.2f}" for c in F1S) + " — the semantic heads reproduce, the absolute-coordinate geometry does not (see stage_bc.md C1).", | |
| } | |
| axes = {a["paper_row"]: a for a in sec2["axes"]} | |
| t2 = { | |
| "mirrored": "4 of 7 axes have a run: w/o ext context (paired, 3 seeds), +Skeleton-Pre (paired, 3–5 seeds), no-prompt feat. (1 seed), no pose head (F1, 3 seeds); RSSM/GRU only as a decoding-order analogue (1 seed); KL bottleneck and Non-causal have no mirror.", | |
| "agreement": (f"Direction agrees with theirs on 'w/o ext context' (geometry ≈ unchanged: ours {axes['w/o ext context']['geometry']['mpjpe_all']['mean_delta']:+.2f} px vs their +0.27; semantics collapse in both), " | |
| f"on '+Skeleton-Pre' geometry (ours {axes['+Skeleton-Pre']['geometry']['CockpitWM_5seeds']['mpjpe_all']['mean_delta']:+.2f} px BEATS vs their −0.55) but NOT on its semantics (ours drop, theirs rise), " | |
| f"and on 'no pose head' DER (ours {axes['no pose head']['f1']['f1_der']['delta']:+.2f} vs their −1.69). Our ablation Δs are an order of magnitude smaller in px than theirs because the " | |
| "keypoint-anchored residual formulation pins the rollout to the last pose; theirs regress absolute coordinates."), | |
| "verdict": "EQUAL in structure (same signs where mirrored), BETTER in evidence (paired CIs over seeds vs single-seed differences); 3 cells open (listed below).", | |
| } | |
| pt = sec3["point"] | |
| by_key = {r["key"]: r for r in sec3["ours"]} | |
| dis_ks = by_key["disable pathway"]["ours"]["KS"]["mean_deviation_px"]["all"]; dis_g = by_key["disable pathway"]["ours"]["KSg"]["mean_deviation_px"]["all"] | |
| dv_ks = by_key["drop view"]["ours"]["KS"]["mean_deviation_px"]["all"]; dv_paper = by_key["drop view"]["paper_delta"]["all"] | |
| t3 = { | |
| "sensitivity": f"their do(Ext=∅) deviation 12.95 px (18.1 % of factual All) vs ours {pt['ours_remove_ext_deviation_px']:.2f} px ({100 * pt['ours_remove_ext_deviation_px'] / pt['ours_factual_all']:.1f} %); " | |
| f"their do(λ_CA=0) {by_key['disable pathway']['paper_delta']['all']:.1f} px vs our token-scaling analogue {dis_ks:.2f} px (gated variant (iii) {dis_g:.2f} px); our reproduction of their architecture: {pt['repro_remove_ext_deviation_px']:.2f} px for do(Ext=∅).", | |
| "accuracy": f"their accuracy without context changes by {pt['paper_accuracy_change_without_context_px_table2']:+.2f} px (Table 2) at 71.47 px; ours changes by {pt['ours_accuracy_change_under_remove_ext_px']:+.2f} px at " | |
| f"{pt['ours_factual_all']:.2f} px — {pt['accuracy_advantage_px_factual']:.1f} px more accurate factual, {pt['accuracy_advantage_px_under_remove_ext_vs_their_factual']:.1f} px more accurate even with the exterior input removed.", | |
| "verdict": f"BETTER: smaller, CI-bounded deviations with the paired accuracy cost reported, at {pt['accuracy_advantage_px_factual']:.0f} px better absolute accuracy; the paper's large deviations are sensitivity without accuracy. Their single-seed Δ carry no CI; the 'drop view' comparison ({dv_paper:.2f} theirs vs {dv_ks:.2f} ours, mean over three views) is the one cell where ours is the more sensitive model, on a 1-of-3-views operator whose paper definition is unspecified.", | |
| } | |
| return {"table1": t1, "table2": t2, "table3": t3, "runs_needed": runs_needed()} | |
| def verdict_md(v): | |
| md = ["## Verdict", "", "### Table 1 — main results", ""] | |
| md += [f"- **Geometry:** {v['table1']['geometry']}", f"- **Semantics (a):** {v['table1']['semantics_a']}", f"- **Semantics (b):** {v['table1']['semantics_b']}", | |
| f"- **Ensemble:** {v['table1']['ensemble']}", f"- **Reproduction:** {v['table1']['reproduction']}", "", "### Table 2 — ablations", "", | |
| f"- {v['table2']['mirrored']}", f"- {v['table2']['agreement']}", f"- **Verdict:** {v['table2']['verdict']}", "", "### Table 3 — interventions", "", | |
| f"- {v['table3']['sensitivity']}", f"- {v['table3']['accuracy']}", f"- **Verdict:** {v['table3']['verdict']}", "", | |
| "### Runs that would complete the mirror (none launched; K ≈ 0.6 GPU-min, KS ≈ 2–3 GPU-min, view-gate heads ≈ 10–13 GPU-min per seed)", "", | |
| md_table(["cell", "run needed", "cost (GPU-min)"], [[r["cell"], r["run"], str(r["cost_gpu_min"])] for r in v["runs_needed"]])] | |
| return "\n".join(md) | |
| # ----------------------------------------------------------------------------------------------- main | |
| UPDATE_TRAININGS = ["cwm3d_recursive_s123_v2", "cwm3d_recursive_s456_v2", "cwm3d_vis_sem_vp_s123_v2", "cwm3d_vis_sem_vp_s456_v2"] | |
| UPDATE_INTERV_RUNS = [r for m in MODELS3_UPDATE_EXTRA for r in m[2]] | |
| PREREG = "docs/decisions.md 2026-09-14 PRE-REGISTRATION (2026-09-15T02:45:58Z) — mirror completion: DESCRIPTIVE, NO ADOPTION" | |
| def run_status(run): | |
| d = RUNS / run | |
| js = sorted(d.glob(f"run_{run}_2*.json")) if d.exists() else [] | |
| if not js: | |
| return {"run": run, "status": "missing"} | |
| j = json.load(open(js[-1])) | |
| m = json.load(open(d / "metrics_test.json"))["metrics"] if (d / "metrics_test.json").exists() else {} | |
| return {"run": run, "status": "done", "gpu_minutes": j.get("gpu_minutes"), "best_epoch": j.get("best_epoch"), "epochs_run": j.get("epochs_run"), | |
| "stop_reason": j.get("stop_reason"), "val_all_best": j.get("best_val_mpjpe_all"), "test_all": m.get("mpjpe_all", {}).get("value"), | |
| "test_hm": m.get("mpjpe_hm", {}).get("value"), "notes": j.get("config", {}).get("notes"), "run_json": str(js[-1].relative_to(ROOT))} | |
| def update_block(store, master, stamp): | |
| tr = [run_status(r) for r in UPDATE_TRAININGS] | |
| iv = [] | |
| for r in UPDATE_INTERV_RUNS: | |
| rec, path = latest_per_run(r) | |
| iv.append({"run": r, "status": "done" if rec else "missing", "file": path, "n_interventions": len(rec["interventions"]) if rec else 0, | |
| "rebuild_maxdiff_px": rec.get("rebuild_maxdiff_px") if rec else None, "factual_all": rec["factual_gt"]["mpjpe_all"] if rec else None}) | |
| s2_md, s2 = section2(store, master) | |
| s2_md = s2_md.replace("## Section 2 —", f"## Section 2 (update {stamp}) —", 1) | |
| models = [MODELS3[0], MODELS3_UPDATE_EXTRA[0], MODELS3_UPDATE_EXTRA[1], MODELS3[1], MODELS3[2]] | |
| S, src = load_interventions_per_run(models) | |
| s3_md, s3 = section3(S, models, source_note="aide/results/runs/<run>/interventions_2*.json (newest per run; the tables' run_interventions_latest.json pointer is not used here)", | |
| heading_suffix=f" (update {stamp})") | |
| axes = {a["paper_row"]: a for a in s2["axes"]} | |
| rec_ax, vp_ax = axes["RSSM/GRU"], axes["no-prompt feat"] | |
| by_key = {r["key"]: r for r in s3["ours"] if r.get("key")} | |
| def cell(key, mkey): | |
| o = by_key[key]["ours"].get(mkey) | |
| return o | |
| lines = [] | |
| ks_r, vg_r, pre_r = cell("remove ext", "KS"), cell("remove ext", "KSvg_a"), cell("remove ext", "KS_pre") | |
| ks_s, vg_s, pre_s = cell("swap", "KS"), cell("swap", "KSvg_a"), cell("swap", "KS_pre") | |
| def dev_txt(o): | |
| return "n/a" if not o else f"{o['mean_deviation_px']['all']:.2f} px ({o['delta_over_factual_all_pct']:.1f} % of factual All {o['mean_factual_gt_all']:.2f}); accuracy cost {o['mean_diff_vs_factual_all']:+.2f} ± {o['sd_diff_vs_factual_all']:.2f} px (CI excl. 0 on {o['n_seeds_ci_excl_0']}/{o['n']} seeds)" | |
| verdict = { | |
| "table2_rssm": f"RSSM/GRU dyn. ↔ recursive decoding, now {rec_ax['geometry']['n_pairs']} seeds: Δ All {fmt_short(rec_ax['geometry'], 'mpjpe_all')} px ({rec_ax['geometry']['mpjpe_all']['verdict']}), " | |
| f"Δ HM {fmt_short(rec_ax['geometry'], 'mpjpe_hm')} ({rec_ax['geometry']['mpjpe_hm']['verdict']}), Δ PCK {fmt_short(rec_ax['geometry'], 'pck05')} ({rec_ax['geometry']['pck05']['verdict']}); " | |
| "their recurrent dynamics cost +6.95 px All — the decoding order is not where their loss comes from.", | |
| "table2_noprompt": f"no-prompt feat. ↔ vis_pooled, now {vp_ax['geometry']['n_pairs']} seeds: Δ All {fmt_short(vp_ax['geometry'], 'mpjpe_all')} px ({vp_ax['geometry']['mpjpe_all']['verdict']}), " | |
| f"Δ HM {fmt_short(vp_ax['geometry'], 'mpjpe_hm')} ({vp_ax['geometry']['mpjpe_hm']['verdict']}); F1 Δ (a) " + fmt_f1b(vp_ax["f1"]) | |
| + "; theirs +5.07 px All and −13.28 TCR — the prompt matters for their absolute-coordinate model, not for a residual forecaster whose heads read the trunk state.", | |
| "table3_viewgate": f"view-gate (a) checkpoints (the model that reaches the published F1): do(Ext=∅) {dev_txt(vg_r)}; do(Ext=Swap) {dev_txt(vg_s)} — vs B2 main do(Ext=∅) {dev_txt(ks_r)}.", | |
| "table3_batontrunk": f"CockpitWM (+ semantic heads) + BATON trunk: do(Ext=∅) {dev_txt(pre_r)}; do(Ext=Swap) {dev_txt(pre_s)}.", | |
| "reading": "Every completed cell keeps the first block's reading: exterior interventions move CockpitWM rollouts by a few px at an accuracy cost of a fraction of a px, at ≈ 23 px better factual accuracy than Driver-WM main, whose do(Ext=∅) deviation is 12.95 px; nothing is adopted or changed in the headline rows.", | |
| } | |
| remaining = [r for r in runs_needed() if not (("recursive decoding at the other locked seeds" in r["cell"]) or ("'no-prompt feat.' at the other locked seeds" in r["cell"]) | |
| or ("Table 3 for the view-gate heads model" in r["cell"]) or ("Table 3 for CockpitWM + BATON trunk" in r["cell"]))] | |
| remaining.append({"cell": "Table 3 for the view-gate reading-(b) checkpoints", "run": "not possible without retraining with checkpoint retention: the `__f1sel` checkpoints were not kept (only pred/metrics)", "cost_gpu_min": "≈ 3 × 10–13 (retrain) + eval"}) | |
| md = [ | |
| "", "", "---", "", | |
| f"## Update {stamp} — mirror completion (pre-registered: {PREREG}; the first block above is unchanged)", | |
| "", | |
| "Runs added by `aide/train/run_mirror_completion.sh` (recipe verbatim, val-only selection, test evaluated once; launch gate: free VRAM ≥ need + 4 GB, MemAvailable ≥ 28 GB, < 2 trainers; log `aide/results/mirror_completion/lane.log`):", | |
| "", | |
| md_table(["training run", "status", "GPU min", "best epoch / epochs / stop", "val All (selection)", "test All", "test HM", "notes"], | |
| [[r["run"], r["status"], f(r.get("gpu_minutes"), 2), f"{r.get('best_epoch')} / {r.get('epochs_run')} / {r.get('stop_reason')}" if r["status"] == "done" else "—", | |
| f(r.get("val_all_best"), 2), f(r.get("test_all"), 2), f(r.get("test_hm"), 2), r.get("notes") or "—"] for r in tr]), | |
| "", | |
| md_table(["intervention pass (eval only, `aide/eval/interventions.py --cuda --n-boot 2000`)", "status", "file", "operators", "rebuild max abs(pred − stored) px", "factual All"], | |
| [[r["run"], r["status"], r["file"] or "—", str(r["n_interventions"]), f(r["rebuild_maxdiff_px"], 6) if r["rebuild_maxdiff_px"] is not None else "—", f(r["factual_all"], 2)] for r in iv]), | |
| "", | |
| s2_md, "", s3_md, "", | |
| "### Verdict (update)", "", | |
| *[f"- {v}" for v in verdict.values()], "", | |
| "### Still open after this update", "", | |
| md_table(["cell", "run needed", "cost (GPU-min)"], [[r["cell"], r["run"], str(r["cost_gpu_min"])] for r in remaining]), | |
| ] | |
| return "\n".join(md), {"stamp": stamp, "prereg": PREREG, "trainings": tr, "interventions": iv, "section2": s2, "section3": s3, "verdict": verdict, "remaining": remaining} | |
| def decisions_update_row(upd): | |
| tr = {r["run"]: r for r in upd["trainings"]} | |
| axes = {a["paper_row"]: a for a in upd["section2"]["axes"]} | |
| rec_g, vp_g = axes["RSSM/GRU"]["geometry"], axes["no-prompt feat"]["geometry"] | |
| by_key = {r["key"]: r for r in upd["section3"]["ours"] if r.get("key")} | |
| def dv(key, m): | |
| o = by_key[key]["ours"].get(m) | |
| return "n/a" if not o else f"{o['mean_deviation_px']['all']:.2f} px, accuracy {o['mean_diff_vs_factual_all']:+.2f} ± {o['sd_diff_vs_factual_all']:.2f} (CI excl. 0 on {o['n_seeds_ci_excl_0']}/{o['n']})" | |
| gpu = sum((r.get("gpu_minutes") or 0) for r in upd["trainings"] if r["status"] == "done") | |
| done_iv = sum(1 for r in upd["interventions"] if r["status"] == "done") | |
| text = (f"**Mirror completion OUTCOME ({upd['stamp']}; descriptive, no adoption; pre-registered 2026-09-15T02:45:58Z).** Trainings ({gpu:.1f} GPU-min, all recipe verbatim, val-only selection, test once): " | |
| + "; ".join(f"`{r}` {tr[r]['status']}" + (f" (test All {tr[r]['test_all']:.2f}, {tr[r]['gpu_minutes']:.2f} GPU-min, best ep {tr[r]['best_epoch']})" if tr[r]["status"] == "done" else "") for r in UPDATE_TRAININGS) | |
| + f". Intervention passes: {done_iv}/{len(upd['interventions'])} done (`__do_*` run dirs + per-run interventions json; the tables' run_interventions_latest.json pointer now points at the last pass and is not used by the report). " | |
| f"**Table 2:** RSSM/GRU dyn. ↔ recursive decoding at {rec_g['n_pairs']} seeds: Δ All {fmt_short(rec_g, 'mpjpe_all')} px ({rec_g['mpjpe_all']['verdict']}), Δ HM {fmt_short(rec_g, 'mpjpe_hm')} ({rec_g['mpjpe_hm']['verdict']}) vs their +6.95; " | |
| f"no-prompt feat. ↔ vis_pooled at {vp_g['n_pairs']} seeds: Δ All {fmt_short(vp_g, 'mpjpe_all')} px ({vp_g['mpjpe_all']['verdict']}), F1 Δ (a) {fmt_f1b(axes['no-prompt feat']['f1'])} vs their +5.07 px / −13.28 TCR. " | |
| f"**Table 3:** view-gate (a) do(Ext=∅) {dv('remove ext', 'KSvg_a')}, do(Swap) {dv('swap', 'KSvg_a')}; CockpitWM (+ semantic heads) + BATON trunk do(Ext=∅) {dv('remove ext', 'KS_pre')}, do(Swap) {dv('swap', 'KS_pre')}; " | |
| f"B2 main reference do(Ext=∅) {dv('remove ext', 'KS')}; the paper's do(Ext=∅) is 12.95 px at 71.47 factual. New block `## Update {upd['stamp']}` appended to `driverwm_tables_headtohead.md` (first block unchanged), JSON key `updates`. " | |
| "Still open: GRU-latent and KL-bottleneck variants (need code), vis_pooled on the view-gate heads (≈ 3 × 10–13 GPU-min), Table 3 on the (b) checkpoints (not kept). A stray LANE_DONE line from the DRY_RUN sits in pids.txt before the real START lines.") | |
| assert "|" not in text | |
| return (f"| 2026-09-14 | {text} | `aide/results/tables/driverwm_tables_headtohead.md` (update block), `.json` (`updates`), `aide/results/mirror_completion/lane.log`, `pids.txt`, run dirs `cwm3d_recursive_s{{123,456}}_v2`, " | |
| f"`cwm3d_vis_sem_vp_s{{123,456}}_v2`, `<run>__do_*` | Nothing adopted; the head-to-head Table-2 mirror is 3-seed on 4 of 7 axes and Table 3 covers the view-gate and BATON-trunk models; paper wording unchanged. |\n") | |
| def append_atomic(path: Path, text: str): | |
| fd = os.open(path, os.O_WRONLY | os.O_APPEND) | |
| try: | |
| os.write(fd, text.encode("utf-8")) | |
| finally: | |
| os.close(fd) | |
| def main(argv=None): | |
| ap = argparse.ArgumentParser() | |
| ap.add_argument("--no-decisions", action="store_true", help="do not append the decisions.md row") | |
| ap.add_argument("--append-block", action="store_true", help="append the mirror-completion update block to the existing md/json instead of regenerating") | |
| ap.add_argument("--preview", action="store_true", help="with --append-block: write the block to aide/results/mirror_completion/preview_headtohead.md only (no md/json/decisions change)") | |
| ap.add_argument("--overwrite", action="store_true", help="allow regenerating the first block over an existing file") | |
| a = ap.parse_args(argv) | |
| t0 = time.time() | |
| stamp = time.strftime("%Y%m%dT%H%M%SZ", time.gmtime()) | |
| master = json.load(open(MASTER)) | |
| store = Store() | |
| if a.append_block: | |
| upd_md, upd = update_block(store, master, stamp) | |
| if a.preview: | |
| out = ROOT / "aide/results/mirror_completion/preview_headtohead.md" | |
| out.parent.mkdir(parents=True, exist_ok=True) | |
| out.write_text(upd_md) | |
| print(f"[headtohead] preview written to {out.relative_to(ROOT)} in {time.time() - t0:.1f} s") | |
| return 0 | |
| assert OUT_MD.exists() and OUT_JSON.exists(), "first block missing; run without --append-block first" | |
| append_atomic(OUT_MD, upd_md + "\n") | |
| J = json.load(open(OUT_JSON)) | |
| J.setdefault("updates", []).append(upd) | |
| OUT_JSON.write_text(json.dumps(J, indent=1, default=float)) | |
| print(f"[headtohead] appended update block {stamp} to {OUT_MD.relative_to(ROOT)} and .json in {time.time() - t0:.1f} s") | |
| if not a.no_decisions: | |
| append_atomic(DECISIONS, decisions_update_row(upd)) | |
| print("[headtohead] appended one decisions.md outcome row") | |
| return 0 | |
| if OUT_MD.exists() and not a.overwrite: | |
| print(f"[headtohead] {OUT_MD.relative_to(ROOT)} exists; pass --overwrite to regenerate the first block or --append-block to add an update") | |
| return 2 | |
| problems, n_seen = cross_check() | |
| s1_md, s1 = section1(store, master) | |
| s2_md, s2 = section2(store, master) | |
| s3_md, s3 = section3() | |
| v = verdict(s1, s2, s3) | |
| n_bad_checks = sum(1 for c in s3["run_output_checks"] if c["match"] is not True) | |
| head = [ | |
| "# Driver-WM paper (arXiv 2605.05092v2) — three tables, row by row, against our AIDE results (official test split, n = 609)", | |
| "", | |
| f"Generated {stamp} by `aide/eval/report_driverwm_headtohead.py`. Naming: **CockpitWM** = our keypoint forecaster; **CockpitWM (+ semantic heads)** = the same trunk with the semantic heads and the exterior " | |
| "Qwen features; **Driver-WM architecture on CockpitWM tokens** = the published architecture fed our keypoint part tokens (shared recipe); **Driver-WM reproduction** = our port of the published model. " | |
| "Sources: our LEVELS from `aide/results/tables/master_aide.json` (report_master_aide.py; " | |
| f"{master['check']['n_runs']} runs recomputed from `pred_test.npz`, max |Δ| vs metrics_test.json = {master['check']['max_abs_diff']:.0e}, {len(master['check']['mismatches'])} mismatches); " | |
| "the 95 % CIs of the seed-mean levels and every paired Δ recomputed here from `pred_test.npz` with `aide/eval/metrics.py` (clip bootstrap 10k, seed 0; F1 2k joint resamples); runs that master_aide.json does not group " | |
| "(kp+sem no-vision (ii), semantic-only, vis_pooled, recursive rollout) evaluated the same way; interventions from `run_interventions_latest.json` with every per-run `__do_*` metrics_test.json re-read " | |
| f"({len(s3['run_output_checks'])} files, {n_bad_checks} mismatches). Locked protocol: driver `first`, grid `floor`, 5 → 5 protocol frames, HM top 60 (threshold {store.hm_thr:.2f} px), PCK@0.05 = {store.pck_thr:.0f} px. " | |
| "Published numbers were typed by the owner in the brief and are the ground truth of what the paper reports.", | |
| "", | |
| "## 0. Cross-check of the typed tables", | |
| "", | |
| (f"Owner-typed Table 1 (12 rows), Table 2 (7 rows) and Table 3 (7 rows) vs `aide/eval/paper_rows.py` (PAPER_ROWS / PAPER_T3_FULL) and vs the {n_seen} published rows rendered in `stage_bc.md` §1b: " | |
| + ("**no discrepancy** (every cell identical)." if not problems else "**DISCREPANCIES:** " + "; ".join(problems))), | |
| "", | |
| ] | |
| md = "\n".join(head) + "\n" + s1_md + "\n\n" + s2_md + "\n\n" + s3_md + "\n\n" + verdict_md(v) + "\n" | |
| out = {"generated_utc": stamp, "script": "aide/eval/report_driverwm_headtohead.py", "n_test": store.N, "protocol": {"hm_threshold_px": store.hm_thr, "pck05_threshold_px": store.pck_thr, "diag_px": P.DIAG}, | |
| "sources": {"levels": str(MASTER.relative_to(ROOT)), "master_check": master["check"], "master_generated_utc": master["generated_utc"], "interventions": str(INTERV.relative_to(ROOT)), | |
| "paired_and_ci": "recomputed from aide/results/runs/<run>/pred_test.npz with aide/eval/metrics.py"}, | |
| "cross_check": {"problems": problems, "stage_bc_rows_checked": n_seen}, | |
| "section1": s1, "section2": s2, "section3": s3, "verdict": v, "wall_s": round(time.time() - t0, 1)} | |
| OUT_MD.write_text(md) | |
| OUT_JSON.write_text(json.dumps(out, indent=1, default=float)) | |
| print(f"[headtohead] wrote {OUT_MD.relative_to(ROOT)} and .json in {time.time() - t0:.1f} s; cross-check problems: {len(problems)}; intervention file checks: {len(s3['run_output_checks'])} ({n_bad_checks} bad)") | |
| if not a.no_decisions: | |
| k3 = s1["margins_K3"]; ours = {r["key"]: r for r in s1["ours"]} | |
| vgb = ours["KSvg_b"]["cells"]; pt = s3["point"] | |
| row = (f"| 2026-09-14 | **Driver-WM three-table head-to-head written** (`aide/results/tables/driverwm_tables_headtohead.md` + .json; generator `aide/eval/report_driverwm_headtohead.py`; " | |
| f"levels from `master_aide.json` ({master['check']['n_runs']} runs, 0 mismatches), CIs / paired Δ recomputed from `pred_test.npz`, interventions from `run_interventions_latest.json` with the per-run `__do_*` metrics re-read; " | |
| f"owner-typed Tables 1–3 vs `paper_rows.py` and `stage_bc.md` §1b: {len(problems)} discrepancies). **Table 1:** CockpitWM (3 seeds) lower / higher than every published row that reports the column (17/17) on all 5 geometry columns " | |
| f"(All {k3['mpjpe_all']['ours_K3']:.2f} vs best published 52.89 Zero-Velocity → {k3['mpjpe_all']['margin']:+.2f} px, vs Driver-WM main {k3['mpjpe_all']['vs_driverwm_main']:+.2f}; HM {k3['mpjpe_hm']['margin']:+.2f} vs Static pooling; PCK {k3['pck05']['margin']:+.2f}); " | |
| f"every published row is ≥ zero-velocity on All (closest: Static pooling 68.50). Semantics: view-gate heads (b) {vgb['f1_dbr']['mean']:.1f} / {vgb['f1_der']['mean']:.1f} / {vgb['f1_tcr']['mean']:.1f} / {vgb['f1_vcr']['mean']:.1f} above Driver-WM main on 4/4 heads, above every published row on VCR only " | |
| f"((a): TCR only); ensemble above every row on all four. Published rows are single-seed and cannot be paired. **Table 2:** 4/7 axes mirrored (w/o ext context: geometry " | |
| f"{s2['axes'][0]['geometry']['mpjpe_all']['mean_delta']:+.2f} px paired vs their +0.27, semantics collapse in both; +Skeleton-Pre: BATON trunk {s2['axes'][2]['geometry']['CockpitWM_5seeds']['mpjpe_all']['mean_delta']:+.2f} px BEATS (5 seeds) vs their −0.55, but our F1 falls where theirs rises; " | |
| f"no-prompt 1 seed; no pose head F1 3 seeds); RSSM/GRU only as the recursive-decoding analogue (+0.18 px, 1 seed); KL bottleneck and Non-causal have no mirror. **Table 3:** their do(Ext=∅) moves the rollout 12.95 px while their accuracy without context changes 0.27 px (sensitivity without accuracy); " | |
| f"ours moves {pt['ours_remove_ext_deviation_px']:.2f} px and costs {pt['ours_accuracy_change_under_remove_ext_px']:+.2f} px at {pt['accuracy_advantage_px_factual']:.1f} px better factual accuracy; our port of their architecture deviates {pt['repro_remove_ext_deviation_px']:.2f} px. " | |
| f"No training launched; the runs that would complete the mirror are listed with cost (recursive at 2 more seeds ≈ 4 GPU-min, vis_pooled at 2 seeds ≈ 5, view-gate interventions eval-only ≈ 1/run; KL / GRU variants need code). | " | |
| f"`aide/results/tables/driverwm_tables_headtohead.md`, `.json`; `master_aide.md`; `run_interventions_latest.json`; `arch_attribution.md` §2; `baton_pretrain.md` §6–8 | " | |
| f"Paper wording: geometry claims 'lower than every published row' per column with the seed-mean CI; semantic claims 'above Driver-WM main on 4/4 heads under (b), single-seed published rows' — never 'SOTA'; Table-3 paragraph: sensitivity without accuracy (theirs) vs small CI-bounded deviations at 23 px better accuracy (ours). |\n") | |
| fd = os.open(DECISIONS, os.O_WRONLY | os.O_APPEND) | |
| try: | |
| os.write(fd, row.encode("utf-8")) # one write, O_APPEND → atomic append of the whole row | |
| finally: | |
| os.close(fd) | |
| print("[headtohead] appended one decisions.md row") | |
| return 0 | |
| if __name__ == "__main__": | |
| sys.exit(main()) | |
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