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, 111 insertions, 0 deletions

diff --git a/worldalign/vg_extract_text.py b/worldalign/vg_extract_text.py
new file mode 100644
index 0000000..affff63
--- /dev/null
+++ b/worldalign/vg_extract_text.py
@@ -0,0 +1,111 @@
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+import torch
+from tqdm import tqdm
+from transformers import AutoModel, AutoTokenizer
+
+from .common import dtype_for_device
+from .extract_text import mean_pool
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser()
+    p.add_argument("--nodes", default="artifacts/vg/text_nodes.jsonl")
+    p.add_argument("--output", default="artifacts/vg/text_features.pt")
+    p.add_argument(
+        "--tier",
+        choices=["region_closed", "visible_relations", "qa_expanded"],
+        default="region_closed",
+    )
+    p.add_argument("--model", default="Qwen/Qwen2.5-1.5B")
+    p.add_argument("--device", default="cuda:3")
+    p.add_argument("--batch-size", type=int, default=96)
+    p.add_argument("--max-length", type=int, default=48)
+    p.add_argument("--layer", type=int, default=-1)
+    p.add_argument("--views", type=int, default=32)
+    p.add_argument("--limit", type=int)
+    return p.parse_args()
+
+
+def read_jsonl(path: str) -> list[dict]:
+    with open(path, encoding="utf-8") as handle:
+        return [json.loads(line) for line in handle if line.strip()]
+
+
+@torch.inference_mode()
+def main() -> None:
+    args = parse_args()
+    records = read_jsonl(args.nodes)
+    if args.limit:
+        records = records[: args.limit]
+    if not records:
+        raise ValueError("No text nodes found")
+    for record in records:
+        if args.tier not in record:
+            raise ValueError(
+                f"Tier {args.tier!r} is absent; regenerate bundles with "
+                "--with-extra-tiers if needed"
+            )
+        if len(record[args.tier]) < args.views:
+            raise ValueError(
+                f"Node {record['node_id']} has fewer than {args.views} views"
+            )
+
+    tokenizer = AutoTokenizer.from_pretrained(args.model)
+    if tokenizer.pad_token_id is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.padding_side = "right"
+    dtype = dtype_for_device(args.device)
+    model = AutoModel.from_pretrained(args.model, torch_dtype=dtype).to(
+        args.device
+    )
+    model.eval()
+
+    flat_texts = [
+        text
+        for record in records
+        for text in record[args.tier][: args.views]
+    ]
+    outputs: list[torch.Tensor] = []
+    for start in tqdm(
+        range(0, len(flat_texts), args.batch_size),
+        desc=f"Qwen VG text:{args.tier}",
+    ):
+        tokens = tokenizer(
+            flat_texts[start : start + args.batch_size],
+            padding=True,
+            truncation=True,
+            max_length=args.max_length,
+            return_tensors="pt",
+        )
+        tokens = {key: value.to(args.device) for key, value in tokens.items()}
+        result = model(
+            **tokens, output_hidden_states=True, return_dict=True
+        )
+        outputs.append(
+            mean_pool(
+                result.hidden_states[args.layer], tokens["attention_mask"]
+            )
+            .float()
+            .cpu()
+        )
+    features = torch.cat(outputs).reshape(len(records), args.views, -1)
+    state = {
+        "model": args.model,
+        "layer": args.layer,
+        "tier": args.tier,
+        "node_ids": [record["node_id"] for record in records],
+        "region_features": features,
+        "views_per_node": args.views,
+    }
+    Path(args.output).parent.mkdir(parents=True, exist_ok=True)
+    torch.save(state, args.output)
+    print(f"Wrote {args.output}: {tuple(features.shape)}")
+
+
+if __name__ == "__main__":
+    main()