World Alignment: unpaired cross-modal correspondence by relational identifiability

Method: scene states are sets of part states; relation fields are built
within each modality and are invariant to how each side labels its own
features; the cross-modal bridge is a coupling searched under an energy
that is a closed-form functional of one matrix; solving is spectral
initialisation followed by exact local refinement.

Evidence: in a procedurally generated closed world, blind recovery of a
hidden image-caption correspondence reaches 95.3% at 256 scenes against
0.39% chance, and the recovered pairs transfer to 200 held-out scenes at
93.0% exact retrieval with random-pair and shuffled-image controls at or
near chance. Cross-modal value correspondence is derived from disjoint
corpora rather than declared. On Visual Genome the field correlation
reaches 0.656 against the 0.9 that polynomial recovery needs, with the
deficit attributed away from segmentation and discretisation.

Protocol: no image-text pair enters any objective, optimiser,
initialisation, or model selection; hidden pairs score orderings only.

Co-Authored-By: Claude <noreply@anthropic.com>

1 files changed, 144 insertions, 0 deletions

diff --git a/worldalign/synth_extract.py b/worldalign/synth_extract.py
new file mode 100644
index 0000000..3186c3e
--- /dev/null
+++ b/worldalign/synth_extract.py
@@ -0,0 +1,144 @@
+"""Feature extraction for the synthetic world, in main-pipeline schema.
+
+Emits vision.pt, text.pt, and text_orbits.pt files with the same fields
+the Flickr loaders read, so the diagnostic, gate, projection, and recovery
+stack runs on the synthetic world unchanged.
+"""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+import torch
+import torch.nn.functional as F
+from tqdm import tqdm
+
+from .common import batch_indices, read_json
+from .synth_towers import TextTower, VisionTower, load_image, tokenize
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data-dir", default="artifacts/synth_v0")
+    parser.add_argument("--vision-tower", default="artifacts/synth_v0/vision_tower.pt")
+    parser.add_argument("--text-tower", default="artifacts/synth_v0/text_tower.pt")
+    parser.add_argument("--batch-size", type=int, default=512)
+    parser.add_argument("--device", default="cuda:3")
+    parser.add_argument("--vision-output", default="artifacts/synth_v0/vision.pt")
+    parser.add_argument("--text-output", default="artifacts/synth_v0/text.pt")
+    parser.add_argument(
+        "--orbits-output", default="artifacts/synth_v0/text_orbits.pt"
+    )
+    return parser.parse_args()
+
+
+@torch.inference_mode()
+def main() -> None:
+    args = parse_args()
+    manifest = read_json(Path(args.data_dir, "manifest.json"))
+    captions = read_json(Path(args.data_dir, "captions.json"))["captions"]
+    image_dir = Path(manifest["image_dir"])
+    views = manifest["visual_views"]
+
+    vision_state = torch.load(args.vision_tower, map_location="cpu", weights_only=False)
+    vision_args = vision_state["args"]
+    vision = VisionTower(
+        manifest["image_size"],
+        vision_args["patch"],
+        vision_args["dim"],
+        vision_args["depth"],
+        vision_args["heads"],
+    ).to(args.device)
+    vision.load_state_dict(vision_state["model"])
+    vision.eval()
+
+    rendered_rows = sorted(
+        set(manifest["vision_only_train"]) | set(manifest["val"]) | set(manifest["test"])
+    )
+    jobs = [(row, view) for row in rendered_rows for view in range(views)]
+    features = []
+    objective = vision_args.get("objective", "infonce")
+    for indices in tqdm(list(batch_indices(len(jobs), args.batch_size)), desc="vision"):
+        pixels = torch.stack(
+            [
+                load_image(image_dir / f"scene{jobs[i][0]:06d}_v{jobs[i][1]}.png")
+                for i in indices
+            ]
+        ).to(args.device)
+        tokens = vision.encode(pixels)
+        state = tokens[:, 1:].mean(1) if objective in ("simmim", "data2vec") else tokens[:, 0]
+        features.append(state.float().cpu())
+    view_features = torch.cat(features).reshape(len(rendered_rows), views, -1)
+    torch.save(
+        {
+            "model": "synth_vision_tower",
+            "rows": rendered_rows,
+            "features": F.normalize(view_features.mean(1), dim=-1),
+            "view_features": view_features,
+            "views_per_scene": views,
+        },
+        args.vision_output,
+    )
+
+    text_state = torch.load(args.text_tower, map_location="cpu", weights_only=False)
+    text_args = text_state["args"]
+    vocab = text_state["vocab"]
+    text = TextTower(
+        len(vocab),
+        text_args["text_dim"],
+        text_args["depth"],
+        text_args.get("text_heads", 4),
+        text_args["context"],
+    ).to(args.device)
+    text.load_state_dict(text_state["model"])
+    text.eval()
+
+    rows = list(range(manifest["all_rows"]))
+    orbit_size = len(captions[0])
+    jobs = [(row, k) for row in rows for k in range(orbit_size)]
+    pooled = []
+    for indices in tqdm(list(batch_indices(len(jobs), args.batch_size)), desc="text"):
+        batch = [tokenize(captions[jobs[i][0]][jobs[i][1]], vocab) for i in indices]
+        longest = min(text_args["context"], max(len(s) for s in batch))
+        tokens = torch.zeros(len(batch), longest, dtype=torch.long)
+        for index, sentence in enumerate(batch):
+            clipped = sentence[:longest]
+            tokens[index, : len(clipped)] = torch.tensor(clipped)
+        tokens = tokens.to(args.device)
+        hidden = text(tokens)
+        mask = (tokens != 0).float()[..., None]
+        pooled.append(
+            ((hidden * mask).sum(1) / mask.sum(1).clamp_min(1.0)).float().cpu()
+        )
+    orbit_features = torch.cat(pooled).reshape(len(rows), orbit_size, -1)
+    torch.save(
+        {
+            "model": "synth_text_tower",
+            "layer": -1,
+            "rows": rows,
+            "features": orbit_features[:, 0],
+            "captions": [captions[row][0] for row in rows],
+            "all_captions": captions,
+        },
+        args.text_output,
+    )
+    torch.save(
+        {
+            "model": "synth_text_tower",
+            "layer": -1,
+            "rows": rows,
+            "features": orbit_features,
+            "captions": captions,
+            "views_per_orbit": orbit_size,
+            "row_groups": ["all"],
+        },
+        args.orbits_output,
+    )
+    print(
+        f"Wrote {args.vision_output}, {args.text_output}, {args.orbits_output}"
+    )
+
+
+if __name__ == "__main__":
+    main()