Datasets:

physics-code-transfer-bench
/

cross-scenario-physics-code-transfer

"""
PRIORITY 1: Cross-scenario transfer test.
All runs use fpa=1 (each of 4 agents sees 1 frame), 4 evenly-spaced frames
per scene from each dataset. This makes collision (24 frames) and ramp
(16 or 8 frames) architecture-compatible.
Protocol:
  - Base training: train sender + receiver on (dataset_src, target).
  - Zero-shot transfer: apply source-trained receiver directly to
    dataset_tgt codes. No retraining.
  - 16-shot calibration: freeze source sender, train new receiver on
    16 stratified examples from dataset_tgt, evaluate on dataset_tgt holdout.
  - Cross-property: freeze source sender, train new receiver on full
    dataset_src train with a different target.
Writes: results/cross_scenario_transfer/
"""
import json, time, sys, os, math, copy
from pathlib import Path
from datetime import datetime, timezone
import numpy as np
import torch
import torch.nn.functional as F

sys.path.insert(0, os.path.dirname(__file__))
from _kinematics_train import (
    load_labels, ClassifierReceiver,
    HIDDEN_DIM, VOCAB_SIZE, N_HEADS, N_AGENTS, MSG_DIM, BATCH_SIZE,
    SENDER_LR, RECEIVER_LR, EARLY_STOP_PATIENCE, DEVICE,
)
from _killer_experiment import (
    TemporalEncoder, DiscreteSender, DiscreteMultiSender,
)

OUT = Path("results/cross_scenario_transfer")
OUT.mkdir(parents=True, exist_ok=True)
LOG = Path("results/overnight_log.txt")

FEATURE_FILES = {
    ("collision", "vjepa2"): "results/vjepa2_collision_pooled.pt",
    ("collision", "dinov2"): "results/collision_dinov2_features.pt",
    ("ramp", "vjepa2"):      "results/vjepa2_ramp_temporal.pt",
    ("ramp", "dinov2"):      "results/phase54b_dino_features.pt",
    ("collision", "clip"):   "results/kinematics_vs_mechanics/clip_collision_features.pt",
    ("ramp", "clip"):        "results/kinematics_vs_mechanics/clip_ramp_features.pt",
}
N_EPOCHS = 150
N_EPOCHS_RECEIVER_ONLY = 100
N_SEEDS = 2
N_FRAMES_SUBSAMPLE = 4  # fpa=1 × N_AGENTS=4 → 4 frames per scene


def log(msg):
    ts = datetime.now(timezone.utc).strftime("%H:%M:%SZ")
    line = f"[{ts}] P1-transfer: {msg}"
    print(line, flush=True)
    LOG.parent.mkdir(parents=True, exist_ok=True)
    with open(LOG, "a") as f: f.write(line + "\n")


def load_and_subsample(dataset, backbone):
    path = FEATURE_FILES[(dataset, backbone)]
    d = torch.load(path, weights_only=False, map_location="cpu")
    feat = d["features"].float()  # (N, T_full, D)
    T_full = feat.shape[1]
    if T_full < N_FRAMES_SUBSAMPLE:
        # Pad by repeating last frame
        pad = feat[:, -1:, :].repeat(1, N_FRAMES_SUBSAMPLE - T_full, 1)
        feat = torch.cat([feat, pad], dim=1)
        idx = list(range(T_full)) + [T_full - 1] * (N_FRAMES_SUBSAMPLE - T_full)
    else:
        idx = np.linspace(0, T_full - 1, N_FRAMES_SUBSAMPLE).astype(int).tolist()
        feat = feat[:, idx, :].contiguous()
    return feat, idx


def extract_clip_ramp():
    """Extract CLIP features for 300 ramp scenes: 4 evenly-spaced frames directly."""
    import timm
    from torchvision import transforms
    from PIL import Image

    out_path = Path("results/kinematics_vs_mechanics/clip_ramp_features.pt")
    if out_path.exists():
        log("CLIP ramp features already cached")
        return

    log("Extracting CLIP ramp features (300 scenes × 24 frames at stride 1)...")
    model = timm.create_model("vit_large_patch14_clip_224.openai",
                               pretrained=True, num_classes=0).to(DEVICE).eval()
    tfm = transforms.Compose([
        transforms.Resize(224), transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
                              std=[0.26862954, 0.26130258, 0.27577711]),
    ])
    DATASET = Path("kubric/output/ramp_dataset")
    n_scenes = 300
    sample = sorted((DATASET / "scene_0000").glob("rgba_*.png"))
    total = len(sample)
    step = max(1, total // 24)
    frame_indices = list(range(0, total, step))[:24]

    feat_out = torch.zeros(n_scenes, 24, 1024, dtype=torch.float32)
    t0 = time.time()
    for si in range(n_scenes):
        sd = DATASET / f"scene_{si:04d}"
        imgs = [tfm(Image.open(sd / f"rgba_{fi:05d}.png").convert("RGB"))
                for fi in frame_indices]
        batch = torch.stack(imgs, 0).to(DEVICE)
        with torch.no_grad():
            feat_out[si] = model(batch).cpu().float()
        if (si + 1) % 100 == 0:
            log(f"  clip-ramp [{si+1}/{n_scenes}] rate={(si+1)/(time.time()-t0):.1f}/s")
            if DEVICE.type == "mps": torch.mps.empty_cache()
    torch.save({"features": feat_out, "frame_indices": frame_indices,
                "model": "vit_large_patch14_clip_224.openai"}, out_path)
    log(f"CLIP ramp done in {time.time()-t0:.0f}s")


def build_sender(feat_dim, fpa):
    senders = [DiscreteSender(TemporalEncoder(HIDDEN_DIM, feat_dim, fpa),
                                HIDDEN_DIM, VOCAB_SIZE, N_HEADS)
               for _ in range(N_AGENTS)]
    return DiscreteMultiSender(senders).to(DEVICE)


def train_base(feat, labels, seed, n_epochs=N_EPOCHS):
    """Train fresh sender+receiver on (feat, labels). Return (sender_state, receiver_state, train_ids, holdout_ids, best_acc)."""
    N, nf, dim = feat.shape
    fpa = 1
    agent_views = [feat[:, i:i+1, :] for i in range(N_AGENTS)]
    torch.manual_seed(seed); np.random.seed(seed)
    rng = np.random.RandomState(seed * 1000 + 42)
    train_ids, holdout_ids = [], []
    for c in np.unique(labels):
        ids_c = np.where(labels == c)[0]
        rng.shuffle(ids_c)
        split = max(1, len(ids_c) // 5)
        holdout_ids.extend(ids_c[:split]); train_ids.extend(ids_c[split:])
    train_ids = np.array(train_ids); holdout_ids = np.array(holdout_ids)
    n_classes = int(labels.max()) + 1
    chance = 1.0 / n_classes

    sender = build_sender(dim, fpa)
    receivers = [ClassifierReceiver(MSG_DIM, HIDDEN_DIM, n_classes).to(DEVICE) for _ in range(3)]
    so = torch.optim.Adam(sender.parameters(), lr=SENDER_LR)
    ros = [torch.optim.Adam(r.parameters(), lr=RECEIVER_LR) for r in receivers]
    labels_dev = torch.tensor(labels, dtype=torch.long).to(DEVICE)
    me = math.log(VOCAB_SIZE)
    n_batches = max(1, len(train_ids) // BATCH_SIZE)
    best_acc, best_ep = 0.0, 0
    best_sender_state, best_receiver_states = None, None

    for ep in range(n_epochs):
        if ep - best_ep > EARLY_STOP_PATIENCE and best_acc > chance + 0.05: break
        if ep > 0 and ep % 40 == 0:
            for i in range(len(receivers)):
                receivers[i] = ClassifierReceiver(MSG_DIM, HIDDEN_DIM, n_classes).to(DEVICE)
                ros[i] = torch.optim.Adam(receivers[i].parameters(), lr=RECEIVER_LR)
        sender.train(); [r.train() for r in receivers]
        tau = 3.0 + (1.0 - 3.0) * ep / max(1, n_epochs - 1)
        hard = ep >= 30
        rng_ep = np.random.RandomState(seed * 10000 + ep)
        perm = rng_ep.permutation(train_ids)
        for b in range(n_batches):
            batch_ids = perm[b*BATCH_SIZE:(b+1)*BATCH_SIZE]
            if len(batch_ids) < 4: continue
            views = [v[batch_ids].to(DEVICE) for v in agent_views]
            tgt = labels_dev[batch_ids]
            msg, logits_list = sender(views, tau=tau, hard=hard)
            loss = torch.tensor(0.0, device=DEVICE)
            for r in receivers: loss = loss + F.cross_entropy(r(msg), tgt)
            loss = loss / len(receivers)
            for lg in logits_list:
                lp = F.log_softmax(lg, -1); p = lp.exp().clamp(min=1e-8)
                ent = -(p * lp).sum(-1).mean()
                if ent / me < 0.1: loss = loss - 0.03 * ent
            if torch.isnan(loss):
                so.zero_grad(); [o.zero_grad() for o in ros]; continue
            so.zero_grad(); [o.zero_grad() for o in ros]
            loss.backward()
            torch.nn.utils.clip_grad_norm_(sender.parameters(), 1.0)
            so.step(); [o.step() for o in ros]
        if ep % 50 == 0 and DEVICE.type == "mps": torch.mps.empty_cache()
        if (ep + 1) % 10 == 0 or ep == 0:
            sender.eval(); [r.eval() for r in receivers]
            with torch.no_grad():
                v_ho = [v[holdout_ids].to(DEVICE) for v in agent_views]
                msg_ho, _ = sender(v_ho)
                tgt_ho = labels_dev[holdout_ids]
                best_per_recv, best_recv_idx = 0.0, 0
                for ri, r in enumerate(receivers):
                    preds = r(msg_ho).argmax(-1)
                    acc = (preds == tgt_ho).float().mean().item()
                    if acc > best_per_recv:
                        best_per_recv, best_recv_idx = acc, ri
                if best_per_recv > best_acc:
                    best_acc, best_ep = best_per_recv, ep
                    best_sender_state = {k: v.cpu().clone() for k, v in sender.state_dict().items()}
                    best_receiver_states = [{k: v.cpu().clone() for k, v in r.state_dict().items()}
                                              for r in receivers]
                    best_recv_idx_saved = best_recv_idx

    return {
        "sender_state": best_sender_state,
        "receiver_states": best_receiver_states,
        "best_recv_idx": best_recv_idx_saved if best_receiver_states else 0,
        "train_ids": train_ids, "holdout_ids": holdout_ids,
        "task_acc": best_acc, "chance": chance,
        "n_classes": n_classes, "fpa": 1, "dim": dim,
    }


def eval_zero_shot(base, feat_tgt, labels_tgt, holdout_ids_tgt):
    """Apply base sender + base receiver directly to target data."""
    N, nf, dim = feat_tgt.shape
    assert dim == base["dim"], f"dim mismatch {dim} vs {base['dim']}"
    sender = build_sender(dim, base["fpa"])
    sender.load_state_dict(base["sender_state"])
    sender.eval().to(DEVICE)
    receivers = [ClassifierReceiver(MSG_DIM, HIDDEN_DIM, base["n_classes"]).to(DEVICE)
                 for _ in range(len(base["receiver_states"]))]
    for r, s in zip(receivers, base["receiver_states"]): r.load_state_dict(s)
    [r.eval() for r in receivers]
    agent_views = [feat_tgt[:, i:i+1, :] for i in range(N_AGENTS)]
    labels_dev = torch.tensor(labels_tgt, dtype=torch.long).to(DEVICE)
    with torch.no_grad():
        v_ho = [v[holdout_ids_tgt].to(DEVICE) for v in agent_views]
        msg_ho, _ = sender(v_ho)
        tgt_ho = labels_dev[holdout_ids_tgt]
        best = 0.0
        for r in receivers:
            preds = r(msg_ho).argmax(-1)
            acc = (preds == tgt_ho).float().mean().item()
            best = max(best, acc)
    return best


def train_receiver_frozen_sender(base, feat_tgt, labels_tgt, train_ids_tgt,
                                   holdout_ids_tgt, seed, n_epochs=N_EPOCHS_RECEIVER_ONLY,
                                   max_examples=None):
    """Freeze base sender. Train NEW receiver on (subset of) train_ids_tgt."""
    N, nf, dim = feat_tgt.shape
    assert dim == base["dim"]
    if max_examples is not None and len(train_ids_tgt) > max_examples:
        rng = np.random.RandomState(seed * 311 + 7)
        picks = []
        per_class = max(1, max_examples // base["n_classes"])
        for c in range(base["n_classes"]):
            ids_c = np.array([i for i in train_ids_tgt if labels_tgt[i] == c])
            if len(ids_c) == 0: continue
            rng.shuffle(ids_c)
            picks.extend(ids_c[:per_class])
        train_ids_tgt = np.array(picks)

    sender = build_sender(dim, base["fpa"])
    sender.load_state_dict(base["sender_state"])
    sender.to(DEVICE).eval()
    for p in sender.parameters(): p.requires_grad = False

    receivers = [ClassifierReceiver(MSG_DIM, HIDDEN_DIM, base["n_classes"]).to(DEVICE) for _ in range(3)]
    ros = [torch.optim.Adam(r.parameters(), lr=RECEIVER_LR) for r in receivers]
    agent_views = [feat_tgt[:, i:i+1, :] for i in range(N_AGENTS)]
    labels_dev = torch.tensor(labels_tgt, dtype=torch.long).to(DEVICE)
    n_batches = max(1, len(train_ids_tgt) // min(BATCH_SIZE, len(train_ids_tgt)))
    best_acc, best_ep = 0.0, 0
    for ep in range(n_epochs):
        if ep - best_ep > EARLY_STOP_PATIENCE and best_acc > base["chance"] + 0.05: break
        [r.train() for r in receivers]
        rng_ep = np.random.RandomState(seed * 10000 + ep)
        perm = rng_ep.permutation(train_ids_tgt)
        bs = min(BATCH_SIZE, len(train_ids_tgt))
        for b in range(max(1, len(train_ids_tgt) // bs)):
            batch_ids = perm[b*bs:(b+1)*bs]
            if len(batch_ids) < 2: continue
            views = [v[batch_ids].to(DEVICE) for v in agent_views]
            with torch.no_grad():
                msg, _ = sender(views)
            for r, o in zip(receivers, ros):
                pred = r(msg)
                loss = F.cross_entropy(pred, labels_dev[batch_ids])
                if torch.isnan(loss): continue
                o.zero_grad(); loss.backward(); o.step()
        if (ep + 1) % 10 == 0 or ep == 0:
            [r.eval() for r in receivers]
            with torch.no_grad():
                v_ho = [v[holdout_ids_tgt].to(DEVICE) for v in agent_views]
                msg_ho, _ = sender(v_ho)
                tgt_ho = labels_dev[holdout_ids_tgt]
                best = 0.0
                for r in receivers:
                    preds = r(msg_ho).argmax(-1)
                    best = max(best, (preds == tgt_ho).float().mean().item())
                if best > best_acc: best_acc, best_ep = best, ep
    return best_acc


def make_splits(labels, seed):
    rng = np.random.RandomState(seed * 1000 + 42)
    train_ids, holdout_ids = [], []
    for c in np.unique(labels):
        ids_c = np.where(labels == c)[0]
        rng.shuffle(ids_c)
        split = max(1, len(ids_c) // 5)
        holdout_ids.extend(ids_c[:split]); train_ids.extend(ids_c[split:])
    return np.array(train_ids), np.array(holdout_ids)


# ── Main ──

def main():
    t_start = time.time()
    log(f"=== OVERNIGHT PRIORITY 1: Cross-Scenario Transfer ===")

    # Extract CLIP ramp if needed
    extract_clip_ramp()

    # Load all features once
    log("Loading features...")
    feats = {}
    for (ds, bb), path in FEATURE_FILES.items():
        if Path(path).exists():
            f, idx = load_and_subsample(ds, bb)
            feats[(ds, bb)] = f
            log(f"  {ds}/{bb}: {tuple(f.shape)}  sampled from T={torch.load(path, weights_only=False, map_location='cpu')['features'].shape[1]}")

    labels_col_restit = load_labels("collision", "restitution")
    labels_col_mass = load_labels("collision", "mass")
    labels_ramp_restit = load_labels("ramp", "restitution")

    all_results = []
    records = []  # for table

    # ── A. Within-scenario sanity (V-JEPA only) ──
    log("\n--- A. Within-scenario sanity ---")
    for seed in range(N_SEEDS):
        log(f"  within collision-restit V-JEPA seed={seed}")
        t0 = time.time()
        r = train_base(feats[("collision", "vjepa2")], labels_col_restit, seed)
        dt = time.time() - t0
        log(f"    acc={r['task_acc']:.3f}  [{dt:.0f}s]")
        records.append({"row": "within_collision_vjepa", "bb": "vjepa2",
                        "seed": seed, "acc": r["task_acc"], "elapsed_s": dt})
        all_results.append({"condition": "within_collision", "backbone": "vjepa2",
                            "seed": seed, "acc": r["task_acc"], "elapsed_s": dt})

    for seed in range(N_SEEDS):
        log(f"  within ramp-restit V-JEPA seed={seed}")
        t0 = time.time()
        r = train_base(feats[("ramp", "vjepa2")], labels_ramp_restit, seed)
        dt = time.time() - t0
        log(f"    acc={r['task_acc']:.3f}  [{dt:.0f}s]")
        records.append({"row": "within_ramp_vjepa", "bb": "vjepa2",
                        "seed": seed, "acc": r["task_acc"], "elapsed_s": dt})
        all_results.append({"condition": "within_ramp", "backbone": "vjepa2",
                            "seed": seed, "acc": r["task_acc"], "elapsed_s": dt})

    # ── Cache base senders needed for transfer ──
    #   col_restit for each backbone × seed
    #   ramp_restit for each backbone × seed
    #   col_mass for V-JEPA × seed  (for cross-property)
    log("\n--- Training base senders for transfer ---")
    bases = {}  # (bb, src_ds, target, seed) -> base dict
    for bb in ["vjepa2", "dinov2", "clip"]:
        if ("collision", bb) not in feats or ("ramp", bb) not in feats: continue
        for seed in range(N_SEEDS):
            log(f"  base {bb} collision-restit seed={seed}")
            t0 = time.time()
            bases[(bb, "collision", "restitution", seed)] = train_base(
                feats[("collision", bb)], labels_col_restit, seed)
            log(f"    acc={bases[(bb, 'collision', 'restitution', seed)]['task_acc']:.3f} [{time.time()-t0:.0f}s]")
            log(f"  base {bb} ramp-restit seed={seed}")
            t0 = time.time()
            bases[(bb, "ramp", "restitution", seed)] = train_base(
                feats[("ramp", bb)], labels_ramp_restit, seed)
            log(f"    acc={bases[(bb, 'ramp', 'restitution', seed)]['task_acc']:.3f} [{time.time()-t0:.0f}s]")
    # V-JEPA collision-mass for cross-property
    for seed in range(N_SEEDS):
        log(f"  base vjepa2 collision-mass seed={seed}")
        t0 = time.time()
        bases[("vjepa2", "collision", "mass", seed)] = train_base(
            feats[("collision", "vjepa2")], labels_col_mass, seed)
        log(f"    acc={bases[('vjepa2', 'collision', 'mass', seed)]['task_acc']:.3f} [{time.time()-t0:.0f}s]")

    # ── B. Cross-scenario transfer ──
    log("\n--- B. Cross-scenario transfer ---")
    for bb in ["vjepa2", "dinov2", "clip"]:
        if (bb, "collision", "restitution", 0) not in bases: continue
        for direction, src_ds, tgt_ds, tgt_labels in [
            ("col_to_ramp", "collision", "ramp", labels_ramp_restit),
            ("ramp_to_col", "ramp", "collision", labels_col_restit),
        ]:
            for seed in range(N_SEEDS):
                base = bases[(bb, src_ds, "restitution", seed)]
                # Splits on the TARGET dataset
                train_ids_tgt, holdout_ids_tgt = make_splits(tgt_labels, seed)
                # Zero-shot
                t0 = time.time()
                acc_zs = eval_zero_shot(base, feats[(tgt_ds, bb)], tgt_labels, holdout_ids_tgt)
                dt_zs = time.time() - t0
                log(f"  {bb} {direction} zero-shot seed={seed}: acc={acc_zs:.3f} [{dt_zs:.1f}s]")
                records.append({"row": f"{direction}_zero_shot", "bb": bb, "seed": seed,
                                 "acc": acc_zs, "elapsed_s": dt_zs})
                all_results.append({"condition": f"{direction}_zero_shot", "backbone": bb,
                                     "seed": seed, "acc": acc_zs, "elapsed_s": dt_zs})
                # 16-shot
                t0 = time.time()
                acc_16 = train_receiver_frozen_sender(
                    base, feats[(tgt_ds, bb)], tgt_labels,
                    train_ids_tgt, holdout_ids_tgt, seed, max_examples=16)
                dt_16 = time.time() - t0
                log(f"  {bb} {direction} 16-shot seed={seed}: acc={acc_16:.3f} [{dt_16:.0f}s]")
                records.append({"row": f"{direction}_16shot", "bb": bb, "seed": seed,
                                 "acc": acc_16, "elapsed_s": dt_16})
                all_results.append({"condition": f"{direction}_16shot", "backbone": bb,
                                     "seed": seed, "acc": acc_16, "elapsed_s": dt_16})

    # ── C. Cross-property controls (V-JEPA, within collision) ──
    log("\n--- C. Cross-property controls (V-JEPA collision) ---")
    # restit-sender → mass
    for seed in range(N_SEEDS):
        base = bases[("vjepa2", "collision", "restitution", seed)]
        train_ids, holdout_ids = make_splits(labels_col_mass, seed)
        t0 = time.time()
        acc = train_receiver_frozen_sender(
            base, feats[("collision", "vjepa2")], labels_col_mass,
            train_ids, holdout_ids, seed, max_examples=None)
        dt = time.time() - t0
        log(f"  V-JEPA restit→mass seed={seed}: acc={acc:.3f} [{dt:.0f}s]")
        records.append({"row": "cross_prop_restit_to_mass", "bb": "vjepa2",
                         "seed": seed, "acc": acc, "elapsed_s": dt})
        all_results.append({"condition": "cross_prop_restit_to_mass",
                             "backbone": "vjepa2", "seed": seed,
                             "acc": acc, "elapsed_s": dt})
    # mass-sender → restit
    for seed in range(N_SEEDS):
        base = bases[("vjepa2", "collision", "mass", seed)]
        train_ids, holdout_ids = make_splits(labels_col_restit, seed)
        t0 = time.time()
        acc = train_receiver_frozen_sender(
            base, feats[("collision", "vjepa2")], labels_col_restit,
            train_ids, holdout_ids, seed, max_examples=None)
        dt = time.time() - t0
        log(f"  V-JEPA mass→restit seed={seed}: acc={acc:.3f} [{dt:.0f}s]")
        records.append({"row": "cross_prop_mass_to_restit", "bb": "vjepa2",
                         "seed": seed, "acc": acc, "elapsed_s": dt})
        all_results.append({"condition": "cross_prop_mass_to_restit",
                             "backbone": "vjepa2", "seed": seed,
                             "acc": acc, "elapsed_s": dt})

    # ── Aggregate + write summary ──
    def agg(cond, bb):
        vals = [r["acc"] for r in all_results
                if r["condition"] == cond and r["backbone"] == bb]
        if not vals: return (float("nan"), float("nan"))
        return (float(np.mean(vals)*100), float(np.std(vals)*100))

    lines = []
    lines.append("CROSS-SCENARIO RESTITUTION TRANSFER")
    lines.append(f"Config: fpa=1, 4 frames (evenly spaced), K=5, 2 seeds/cell.")
    lines.append("")
    header = "Condition                              | V-JEPA 2      | DINOv2        | CLIP          | Chance"
    lines.append(header)
    lines.append("-" * len(header))
    def row(name, cond, bbs=("vjepa2", "dinov2", "clip")):
        cells = []
        for bb in bbs:
            m, s = agg(cond, bb)
            if np.isnan(m):
                cells.append("     —        ")
            else:
                cells.append(f"{m:5.1f}% ± {s:4.1f} ")
        return f"{name:<39s}| {cells[0]}| {cells[1]}| {cells[2]}| 33.3%"
    lines.append(row("Within collision (sanity)", "within_collision", ("vjepa2",)))
    lines.append(row("Within ramp (sanity)",       "within_ramp",       ("vjepa2",)))
    lines.append(row("Collision→Ramp (zero-shot)", "col_to_ramp_zero_shot"))
    lines.append(row("Ramp→Collision (zero-shot)", "ramp_to_col_zero_shot"))
    lines.append(row("Collision→Ramp (16-shot)",   "col_to_ramp_16shot"))
    lines.append(row("Ramp→Collision (16-shot)",   "ramp_to_col_16shot"))
    lines.append(row("Cross-property: restit→mass","cross_prop_restit_to_mass", ("vjepa2",)))
    lines.append(row("Cross-property: mass→restit","cross_prop_mass_to_restit", ("vjepa2",)))

    total_s = time.time() - t_start
    lines.append("")
    lines.append(f"Total runtime: {total_s/60:.1f} min ({total_s:.0f}s)")
    lines.append(f"Runs: {len(all_results)}")
    summary = "\n".join(lines)

    (OUT / "p1_summary.txt").write_text(summary + "\n")
    with open(OUT / "p1_raw.json", "w") as f:
        # remove state dicts from bases for serialization
        json.dump({"runs": all_results,
                   "n_runs": len(all_results),
                   "total_runtime_s": total_s},
                  f, indent=2, default=str)
    log(f"\n{summary}")
    log(f"Saved: {OUT / 'p1_summary.txt'}")


if __name__ == "__main__":
    main()

	"""
	PRIORITY 1: Cross-scenario transfer test.

	All runs use fpa=1 (each of 4 agents sees 1 frame), 4 evenly-spaced frames
	per scene from each dataset. This makes collision (24 frames) and ramp
	(16 or 8 frames) architecture-compatible.

	Protocol:
	- Base training: train sender + receiver on (dataset_src, target).
	- Zero-shot transfer: apply source-trained receiver directly to
	dataset_tgt codes. No retraining.
	- 16-shot calibration: freeze source sender, train new receiver on
	16 stratified examples from dataset_tgt, evaluate on dataset_tgt holdout.
	- Cross-property: freeze source sender, train new receiver on full
	dataset_src train with a different target.

	Writes: results/cross_scenario_transfer/
	"""
	import json, time, sys, os, math, copy
	from pathlib import Path
	from datetime import datetime, timezone
	import numpy as np
	import torch
	import torch.nn.functional as F

	sys.path.insert(0, os.path.dirname(__file__))
	from _kinematics_train import (
	load_labels, ClassifierReceiver,
	HIDDEN_DIM, VOCAB_SIZE, N_HEADS, N_AGENTS, MSG_DIM, BATCH_SIZE,
	SENDER_LR, RECEIVER_LR, EARLY_STOP_PATIENCE, DEVICE,
	)
	from _killer_experiment import (
	TemporalEncoder, DiscreteSender, DiscreteMultiSender,
	)

	OUT = Path("results/cross_scenario_transfer")
	OUT.mkdir(parents=True, exist_ok=True)
	LOG = Path("results/overnight_log.txt")

	FEATURE_FILES = {
	("collision", "vjepa2"): "results/vjepa2_collision_pooled.pt",
	("collision", "dinov2"): "results/collision_dinov2_features.pt",
	("ramp", "vjepa2"): "results/vjepa2_ramp_temporal.pt",
	("ramp", "dinov2"): "results/phase54b_dino_features.pt",
	("collision", "clip"): "results/kinematics_vs_mechanics/clip_collision_features.pt",
	("ramp", "clip"): "results/kinematics_vs_mechanics/clip_ramp_features.pt",
	}
	N_EPOCHS = 150
	N_EPOCHS_RECEIVER_ONLY = 100
	N_SEEDS = 2
	N_FRAMES_SUBSAMPLE = 4 # fpa=1 × N_AGENTS=4 → 4 frames per scene


	def log(msg):
	ts = datetime.now(timezone.utc).strftime("%H:%M:%SZ")
	line = f"[{ts}] P1-transfer: {msg}"
	print(line, flush=True)
	LOG.parent.mkdir(parents=True, exist_ok=True)
	with open(LOG, "a") as f: f.write(line + "\n")


	def load_and_subsample(dataset, backbone):
	path = FEATURE_FILES[(dataset, backbone)]
	d = torch.load(path, weights_only=False, map_location="cpu")
	feat = d["features"].float() # (N, T_full, D)
	T_full = feat.shape[1]
	if T_full < N_FRAMES_SUBSAMPLE:
	# Pad by repeating last frame
	pad = feat[:, -1:, :].repeat(1, N_FRAMES_SUBSAMPLE - T_full, 1)
	feat = torch.cat([feat, pad], dim=1)
	idx = list(range(T_full)) + [T_full - 1] * (N_FRAMES_SUBSAMPLE - T_full)
	else:
	idx = np.linspace(0, T_full - 1, N_FRAMES_SUBSAMPLE).astype(int).tolist()
	feat = feat[:, idx, :].contiguous()
	return feat, idx


	def extract_clip_ramp():
	"""Extract CLIP features for 300 ramp scenes: 4 evenly-spaced frames directly."""
	import timm
	from torchvision import transforms
	from PIL import Image

	out_path = Path("results/kinematics_vs_mechanics/clip_ramp_features.pt")
	if out_path.exists():
	log("CLIP ramp features already cached")
	return

	log("Extracting CLIP ramp features (300 scenes × 24 frames at stride 1)...")
	model = timm.create_model("vit_large_patch14_clip_224.openai",
	pretrained=True, num_classes=0).to(DEVICE).eval()
	tfm = transforms.Compose([
	transforms.Resize(224), transforms.CenterCrop(224),
	transforms.ToTensor(),
	transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
	std=[0.26862954, 0.26130258, 0.27577711]),
	])
	DATASET = Path("kubric/output/ramp_dataset")
	n_scenes = 300
	sample = sorted((DATASET / "scene_0000").glob("rgba_*.png"))
	total = len(sample)
	step = max(1, total // 24)
	frame_indices = list(range(0, total, step))[:24]

	feat_out = torch.zeros(n_scenes, 24, 1024, dtype=torch.float32)
	t0 = time.time()
	for si in range(n_scenes):
	sd = DATASET / f"scene_{si:04d}"
	imgs = [tfm(Image.open(sd / f"rgba_{fi:05d}.png").convert("RGB"))
	for fi in frame_indices]
	batch = torch.stack(imgs, 0).to(DEVICE)
	with torch.no_grad():
	feat_out[si] = model(batch).cpu().float()
	if (si + 1) % 100 == 0:
	log(f" clip-ramp [{si+1}/{n_scenes}] rate={(si+1)/(time.time()-t0):.1f}/s")
	if DEVICE.type == "mps": torch.mps.empty_cache()
	torch.save({"features": feat_out, "frame_indices": frame_indices,
	"model": "vit_large_patch14_clip_224.openai"}, out_path)
	log(f"CLIP ramp done in {time.time()-t0:.0f}s")


	def build_sender(feat_dim, fpa):
	senders = [DiscreteSender(TemporalEncoder(HIDDEN_DIM, feat_dim, fpa),
	HIDDEN_DIM, VOCAB_SIZE, N_HEADS)
	for _ in range(N_AGENTS)]
	return DiscreteMultiSender(senders).to(DEVICE)


	def train_base(feat, labels, seed, n_epochs=N_EPOCHS):
	"""Train fresh sender+receiver on (feat, labels). Return (sender_state, receiver_state, train_ids, holdout_ids, best_acc)."""
	N, nf, dim = feat.shape
	fpa = 1
	agent_views = [feat[:, i:i+1, :] for i in range(N_AGENTS)]
	torch.manual_seed(seed); np.random.seed(seed)
	rng = np.random.RandomState(seed * 1000 + 42)
	train_ids, holdout_ids = [], []
	for c in np.unique(labels):
	ids_c = np.where(labels == c)[0]
	rng.shuffle(ids_c)
	split = max(1, len(ids_c) // 5)
	holdout_ids.extend(ids_c[:split]); train_ids.extend(ids_c[split:])
	train_ids = np.array(train_ids); holdout_ids = np.array(holdout_ids)
	n_classes = int(labels.max()) + 1
	chance = 1.0 / n_classes

	sender = build_sender(dim, fpa)
	receivers = [ClassifierReceiver(MSG_DIM, HIDDEN_DIM, n_classes).to(DEVICE) for _ in range(3)]
	so = torch.optim.Adam(sender.parameters(), lr=SENDER_LR)
	ros = [torch.optim.Adam(r.parameters(), lr=RECEIVER_LR) for r in receivers]
	labels_dev = torch.tensor(labels, dtype=torch.long).to(DEVICE)
	me = math.log(VOCAB_SIZE)
	n_batches = max(1, len(train_ids) // BATCH_SIZE)
	best_acc, best_ep = 0.0, 0
	best_sender_state, best_receiver_states = None, None

	for ep in range(n_epochs):
	if ep - best_ep > EARLY_STOP_PATIENCE and best_acc > chance + 0.05: break
	if ep > 0 and ep % 40 == 0:
	for i in range(len(receivers)):
	receivers[i] = ClassifierReceiver(MSG_DIM, HIDDEN_DIM, n_classes).to(DEVICE)
	ros[i] = torch.optim.Adam(receivers[i].parameters(), lr=RECEIVER_LR)
	sender.train(); [r.train() for r in receivers]
	tau = 3.0 + (1.0 - 3.0) * ep / max(1, n_epochs - 1)
	hard = ep >= 30
	rng_ep = np.random.RandomState(seed * 10000 + ep)
	perm = rng_ep.permutation(train_ids)
	for b in range(n_batches):
	batch_ids = perm[bBATCH_SIZE:(b+1)BATCH_SIZE]
	if len(batch_ids) < 4: continue
	views = [v[batch_ids].to(DEVICE) for v in agent_views]
	tgt = labels_dev[batch_ids]
	msg, logits_list = sender(views, tau=tau, hard=hard)
	loss = torch.tensor(0.0, device=DEVICE)
	for r in receivers: loss = loss + F.cross_entropy(r(msg), tgt)
	loss = loss / len(receivers)
	for lg in logits_list:
	lp = F.log_softmax(lg, -1); p = lp.exp().clamp(min=1e-8)
	ent = -(p * lp).sum(-1).mean()
	if ent / me < 0.1: loss = loss - 0.03 * ent
	if torch.isnan(loss):
	so.zero_grad(); [o.zero_grad() for o in ros]; continue
	so.zero_grad(); [o.zero_grad() for o in ros]
	loss.backward()
	torch.nn.utils.clip_grad_norm_(sender.parameters(), 1.0)
	so.step(); [o.step() for o in ros]
	if ep % 50 == 0 and DEVICE.type == "mps": torch.mps.empty_cache()
	if (ep + 1) % 10 == 0 or ep == 0:
	sender.eval(); [r.eval() for r in receivers]
	with torch.no_grad():
	v_ho = [v[holdout_ids].to(DEVICE) for v in agent_views]
	msg_ho, _ = sender(v_ho)
	tgt_ho = labels_dev[holdout_ids]
	best_per_recv, best_recv_idx = 0.0, 0
	for ri, r in enumerate(receivers):
	preds = r(msg_ho).argmax(-1)
	acc = (preds == tgt_ho).float().mean().item()
	if acc > best_per_recv:
	best_per_recv, best_recv_idx = acc, ri
	if best_per_recv > best_acc:
	best_acc, best_ep = best_per_recv, ep
	best_sender_state = {k: v.cpu().clone() for k, v in sender.state_dict().items()}
	best_receiver_states = [{k: v.cpu().clone() for k, v in r.state_dict().items()}
	for r in receivers]
	best_recv_idx_saved = best_recv_idx

	return {
	"sender_state": best_sender_state,
	"receiver_states": best_receiver_states,
	"best_recv_idx": best_recv_idx_saved if best_receiver_states else 0,
	"train_ids": train_ids, "holdout_ids": holdout_ids,
	"task_acc": best_acc, "chance": chance,
	"n_classes": n_classes, "fpa": 1, "dim": dim,
	}


	def eval_zero_shot(base, feat_tgt, labels_tgt, holdout_ids_tgt):
	"""Apply base sender + base receiver directly to target data."""
	N, nf, dim = feat_tgt.shape
	assert dim == base["dim"], f"dim mismatch {dim} vs {base['dim']}"
	sender = build_sender(dim, base["fpa"])
	sender.load_state_dict(base["sender_state"])
	sender.eval().to(DEVICE)
	receivers = [ClassifierReceiver(MSG_DIM, HIDDEN_DIM, base["n_classes"]).to(DEVICE)
	for _ in range(len(base["receiver_states"]))]
	for r, s in zip(receivers, base["receiver_states"]): r.load_state_dict(s)
	[r.eval() for r in receivers]
	agent_views = [feat_tgt[:, i:i+1, :] for i in range(N_AGENTS)]
	labels_dev = torch.tensor(labels_tgt, dtype=torch.long).to(DEVICE)
	with torch.no_grad():
	v_ho = [v[holdout_ids_tgt].to(DEVICE) for v in agent_views]
	msg_ho, _ = sender(v_ho)
	tgt_ho = labels_dev[holdout_ids_tgt]
	best = 0.0
	for r in receivers:
	preds = r(msg_ho).argmax(-1)
	acc = (preds == tgt_ho).float().mean().item()
	best = max(best, acc)
	return best


	def train_receiver_frozen_sender(base, feat_tgt, labels_tgt, train_ids_tgt,
	holdout_ids_tgt, seed, n_epochs=N_EPOCHS_RECEIVER_ONLY,
	max_examples=None):
	"""Freeze base sender. Train NEW receiver on (subset of) train_ids_tgt."""
	N, nf, dim = feat_tgt.shape
	assert dim == base["dim"]
	if max_examples is not None and len(train_ids_tgt) > max_examples:
	rng = np.random.RandomState(seed * 311 + 7)
	picks = []
	per_class = max(1, max_examples // base["n_classes"])
	for c in range(base["n_classes"]):
	ids_c = np.array([i for i in train_ids_tgt if labels_tgt[i] == c])
	if len(ids_c) == 0: continue
	rng.shuffle(ids_c)
	picks.extend(ids_c[:per_class])
	train_ids_tgt = np.array(picks)

	sender = build_sender(dim, base["fpa"])
	sender.load_state_dict(base["sender_state"])
	sender.to(DEVICE).eval()
	for p in sender.parameters(): p.requires_grad = False

	receivers = [ClassifierReceiver(MSG_DIM, HIDDEN_DIM, base["n_classes"]).to(DEVICE) for _ in range(3)]
	ros = [torch.optim.Adam(r.parameters(), lr=RECEIVER_LR) for r in receivers]
	agent_views = [feat_tgt[:, i:i+1, :] for i in range(N_AGENTS)]
	labels_dev = torch.tensor(labels_tgt, dtype=torch.long).to(DEVICE)
	n_batches = max(1, len(train_ids_tgt) // min(BATCH_SIZE, len(train_ids_tgt)))
	best_acc, best_ep = 0.0, 0
	for ep in range(n_epochs):
	if ep - best_ep > EARLY_STOP_PATIENCE and best_acc > base["chance"] + 0.05: break
	[r.train() for r in receivers]
	rng_ep = np.random.RandomState(seed * 10000 + ep)
	perm = rng_ep.permutation(train_ids_tgt)
	bs = min(BATCH_SIZE, len(train_ids_tgt))
	for b in range(max(1, len(train_ids_tgt) // bs)):
	batch_ids = perm[bbs:(b+1)bs]
	if len(batch_ids) < 2: continue
	views = [v[batch_ids].to(DEVICE) for v in agent_views]
	with torch.no_grad():
	msg, _ = sender(views)
	for r, o in zip(receivers, ros):
	pred = r(msg)
	loss = F.cross_entropy(pred, labels_dev[batch_ids])
	if torch.isnan(loss): continue
	o.zero_grad(); loss.backward(); o.step()
	if (ep + 1) % 10 == 0 or ep == 0:
	[r.eval() for r in receivers]
	with torch.no_grad():
	v_ho = [v[holdout_ids_tgt].to(DEVICE) for v in agent_views]
	msg_ho, _ = sender(v_ho)
	tgt_ho = labels_dev[holdout_ids_tgt]
	best = 0.0
	for r in receivers:
	preds = r(msg_ho).argmax(-1)
	best = max(best, (preds == tgt_ho).float().mean().item())
	if best > best_acc: best_acc, best_ep = best, ep
	return best_acc


	def make_splits(labels, seed):
	rng = np.random.RandomState(seed * 1000 + 42)
	train_ids, holdout_ids = [], []
	for c in np.unique(labels):
	ids_c = np.where(labels == c)[0]
	rng.shuffle(ids_c)
	split = max(1, len(ids_c) // 5)
	holdout_ids.extend(ids_c[:split]); train_ids.extend(ids_c[split:])
	return np.array(train_ids), np.array(holdout_ids)


	# ── Main ──

	def main():
	t_start = time.time()
	log(f"=== OVERNIGHT PRIORITY 1: Cross-Scenario Transfer ===")

	# Extract CLIP ramp if needed
	extract_clip_ramp()

	# Load all features once
	log("Loading features...")
	feats = {}
	for (ds, bb), path in FEATURE_FILES.items():
	if Path(path).exists():
	f, idx = load_and_subsample(ds, bb)
	feats[(ds, bb)] = f
	log(f" {ds}/{bb}: {tuple(f.shape)} sampled from T={torch.load(path, weights_only=False, map_location='cpu')['features'].shape[1]}")

	labels_col_restit = load_labels("collision", "restitution")
	labels_col_mass = load_labels("collision", "mass")
	labels_ramp_restit = load_labels("ramp", "restitution")

	all_results = []
	records = [] # for table

	# ── A. Within-scenario sanity (V-JEPA only) ──
	log("\n--- A. Within-scenario sanity ---")
	for seed in range(N_SEEDS):
	log(f" within collision-restit V-JEPA seed={seed}")
	t0 = time.time()
	r = train_base(feats[("collision", "vjepa2")], labels_col_restit, seed)
	dt = time.time() - t0
	log(f" acc={r['task_acc']:.3f} [{dt:.0f}s]")
	records.append({"row": "within_collision_vjepa", "bb": "vjepa2",
	"seed": seed, "acc": r["task_acc"], "elapsed_s": dt})
	all_results.append({"condition": "within_collision", "backbone": "vjepa2",
	"seed": seed, "acc": r["task_acc"], "elapsed_s": dt})

	for seed in range(N_SEEDS):
	log(f" within ramp-restit V-JEPA seed={seed}")
	t0 = time.time()
	r = train_base(feats[("ramp", "vjepa2")], labels_ramp_restit, seed)
	dt = time.time() - t0
	log(f" acc={r['task_acc']:.3f} [{dt:.0f}s]")
	records.append({"row": "within_ramp_vjepa", "bb": "vjepa2",
	"seed": seed, "acc": r["task_acc"], "elapsed_s": dt})
	all_results.append({"condition": "within_ramp", "backbone": "vjepa2",
	"seed": seed, "acc": r["task_acc"], "elapsed_s": dt})

	# ── Cache base senders needed for transfer ──
	# col_restit for each backbone × seed
	# ramp_restit for each backbone × seed
	# col_mass for V-JEPA × seed (for cross-property)
	log("\n--- Training base senders for transfer ---")
	bases = {} # (bb, src_ds, target, seed) -> base dict
	for bb in ["vjepa2", "dinov2", "clip"]:
	if ("collision", bb) not in feats or ("ramp", bb) not in feats: continue
	for seed in range(N_SEEDS):
	log(f" base {bb} collision-restit seed={seed}")
	t0 = time.time()
	bases[(bb, "collision", "restitution", seed)] = train_base(
	feats[("collision", bb)], labels_col_restit, seed)
	log(f" acc={bases[(bb, 'collision', 'restitution', seed)]['task_acc']:.3f} [{time.time()-t0:.0f}s]")
	log(f" base {bb} ramp-restit seed={seed}")
	t0 = time.time()
	bases[(bb, "ramp", "restitution", seed)] = train_base(
	feats[("ramp", bb)], labels_ramp_restit, seed)
	log(f" acc={bases[(bb, 'ramp', 'restitution', seed)]['task_acc']:.3f} [{time.time()-t0:.0f}s]")
	# V-JEPA collision-mass for cross-property
	for seed in range(N_SEEDS):
	log(f" base vjepa2 collision-mass seed={seed}")
	t0 = time.time()
	bases[("vjepa2", "collision", "mass", seed)] = train_base(
	feats[("collision", "vjepa2")], labels_col_mass, seed)
	log(f" acc={bases[('vjepa2', 'collision', 'mass', seed)]['task_acc']:.3f} [{time.time()-t0:.0f}s]")

	# ── B. Cross-scenario transfer ──
	log("\n--- B. Cross-scenario transfer ---")
	for bb in ["vjepa2", "dinov2", "clip"]:
	if (bb, "collision", "restitution", 0) not in bases: continue
	for direction, src_ds, tgt_ds, tgt_labels in [
	("col_to_ramp", "collision", "ramp", labels_ramp_restit),
	("ramp_to_col", "ramp", "collision", labels_col_restit),
	]:
	for seed in range(N_SEEDS):
	base = bases[(bb, src_ds, "restitution", seed)]
	# Splits on the TARGET dataset
	train_ids_tgt, holdout_ids_tgt = make_splits(tgt_labels, seed)
	# Zero-shot
	t0 = time.time()
	acc_zs = eval_zero_shot(base, feats[(tgt_ds, bb)], tgt_labels, holdout_ids_tgt)
	dt_zs = time.time() - t0
	log(f" {bb} {direction} zero-shot seed={seed}: acc={acc_zs:.3f} [{dt_zs:.1f}s]")
	records.append({"row": f"{direction}_zero_shot", "bb": bb, "seed": seed,
	"acc": acc_zs, "elapsed_s": dt_zs})
	all_results.append({"condition": f"{direction}_zero_shot", "backbone": bb,
	"seed": seed, "acc": acc_zs, "elapsed_s": dt_zs})
	# 16-shot
	t0 = time.time()
	acc_16 = train_receiver_frozen_sender(
	base, feats[(tgt_ds, bb)], tgt_labels,
	train_ids_tgt, holdout_ids_tgt, seed, max_examples=16)
	dt_16 = time.time() - t0
	log(f" {bb} {direction} 16-shot seed={seed}: acc={acc_16:.3f} [{dt_16:.0f}s]")
	records.append({"row": f"{direction}_16shot", "bb": bb, "seed": seed,
	"acc": acc_16, "elapsed_s": dt_16})
	all_results.append({"condition": f"{direction}_16shot", "backbone": bb,
	"seed": seed, "acc": acc_16, "elapsed_s": dt_16})

	# ── C. Cross-property controls (V-JEPA, within collision) ──
	log("\n--- C. Cross-property controls (V-JEPA collision) ---")
	# restit-sender → mass
	for seed in range(N_SEEDS):
	base = bases[("vjepa2", "collision", "restitution", seed)]
	train_ids, holdout_ids = make_splits(labels_col_mass, seed)
	t0 = time.time()
	acc = train_receiver_frozen_sender(
	base, feats[("collision", "vjepa2")], labels_col_mass,
	train_ids, holdout_ids, seed, max_examples=None)
	dt = time.time() - t0
	log(f" V-JEPA restit→mass seed={seed}: acc={acc:.3f} [{dt:.0f}s]")
	records.append({"row": "cross_prop_restit_to_mass", "bb": "vjepa2",
	"seed": seed, "acc": acc, "elapsed_s": dt})
	all_results.append({"condition": "cross_prop_restit_to_mass",
	"backbone": "vjepa2", "seed": seed,
	"acc": acc, "elapsed_s": dt})
	# mass-sender → restit
	for seed in range(N_SEEDS):
	base = bases[("vjepa2", "collision", "mass", seed)]
	train_ids, holdout_ids = make_splits(labels_col_restit, seed)
	t0 = time.time()
	acc = train_receiver_frozen_sender(
	base, feats[("collision", "vjepa2")], labels_col_restit,
	train_ids, holdout_ids, seed, max_examples=None)
	dt = time.time() - t0
	log(f" V-JEPA mass→restit seed={seed}: acc={acc:.3f} [{dt:.0f}s]")
	records.append({"row": "cross_prop_mass_to_restit", "bb": "vjepa2",
	"seed": seed, "acc": acc, "elapsed_s": dt})
	all_results.append({"condition": "cross_prop_mass_to_restit",
	"backbone": "vjepa2", "seed": seed,
	"acc": acc, "elapsed_s": dt})

	# ── Aggregate + write summary ──
	def agg(cond, bb):
	vals = [r["acc"] for r in all_results
	if r["condition"] == cond and r["backbone"] == bb]
	if not vals: return (float("nan"), float("nan"))
	return (float(np.mean(vals)100), float(np.std(vals)100))

	lines = []
	lines.append("CROSS-SCENARIO RESTITUTION TRANSFER")
	lines.append(f"Config: fpa=1, 4 frames (evenly spaced), K=5, 2 seeds/cell.")
	lines.append("")
	header = "Condition \| V-JEPA 2 \| DINOv2 \| CLIP \| Chance"
	lines.append(header)
	lines.append("-" * len(header))
	def row(name, cond, bbs=("vjepa2", "dinov2", "clip")):
	cells = []
	for bb in bbs:
	m, s = agg(cond, bb)
	if np.isnan(m):
	cells.append(" — ")
	else:
	cells.append(f"{m:5.1f}% ± {s:4.1f} ")
	return f"{name:<39s}\| {cells[0]}\| {cells[1]}\| {cells[2]}\| 33.3%"
	lines.append(row("Within collision (sanity)", "within_collision", ("vjepa2",)))
	lines.append(row("Within ramp (sanity)", "within_ramp", ("vjepa2",)))
	lines.append(row("Collision→Ramp (zero-shot)", "col_to_ramp_zero_shot"))
	lines.append(row("Ramp→Collision (zero-shot)", "ramp_to_col_zero_shot"))
	lines.append(row("Collision→Ramp (16-shot)", "col_to_ramp_16shot"))
	lines.append(row("Ramp→Collision (16-shot)", "ramp_to_col_16shot"))
	lines.append(row("Cross-property: restit→mass","cross_prop_restit_to_mass", ("vjepa2",)))
	lines.append(row("Cross-property: mass→restit","cross_prop_mass_to_restit", ("vjepa2",)))

	total_s = time.time() - t_start
	lines.append("")
	lines.append(f"Total runtime: {total_s/60:.1f} min ({total_s:.0f}s)")
	lines.append(f"Runs: {len(all_results)}")
	summary = "\n".join(lines)

	(OUT / "p1_summary.txt").write_text(summary + "\n")
	with open(OUT / "p1_raw.json", "w") as f:
	# remove state dicts from bases for serialization
	json.dump({"runs": all_results,
	"n_runs": len(all_results),
	"total_runtime_s": total_s},
	f, indent=2, default=str)
	log(f"\n{summary}")
	log(f"Saved: {OUT / 'p1_summary.txt'}")


	if __name__ == "__main__":
	main()