From a62cf4d2a99b4a7985c61b2a7feb92a82a8218b7 Mon Sep 17 00:00:00 2001
From: Yuren Hao <blackhao0426@gmail.com>
Date: Sat, 1 Aug 2026 14:10:03 -0500
Subject: 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>
---
 worldalign/prepare.py | 80 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 80 insertions(+)
 create mode 100644 worldalign/prepare.py

(limited to 'worldalign/prepare.py')

diff --git a/worldalign/prepare.py b/worldalign/prepare.py
new file mode 100644
index 0000000..dbdd101
--- /dev/null
+++ b/worldalign/prepare.py
@@ -0,0 +1,80 @@
+from __future__ import annotations
+
+import argparse
+from collections import Counter
+
+import numpy as np
+from datasets import load_dataset
+
+from .common import DATASET_NAME, DATASET_SPLIT, write_json
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser()
+    p.add_argument("--output", default="artifacts/manifest.json")
+    p.add_argument("--dataset", default=DATASET_NAME)
+    p.add_argument("--seed", type=int, default=20260728)
+    p.add_argument("--unpaired-per-modality", type=int, default=12_000)
+    p.add_argument("--paired-train", type=int, default=12_000)
+    p.add_argument("--max-eval", type=int, default=1_000)
+    return p.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    dataset = load_dataset(args.dataset, split=DATASET_SPLIT)
+    by_split: dict[str, list[int]] = {}
+    for i, split in enumerate(dataset["split"]):
+        by_split.setdefault(split, []).append(i)
+
+    counts = Counter(dataset["split"])
+    print(f"Internal Flickr splits: {dict(counts)}")
+    train = np.asarray(by_split["train"], dtype=np.int64)
+    rng = np.random.default_rng(args.seed)
+    rng.shuffle(train)
+
+    n_unpaired = min(args.unpaired_per_modality, len(train) // 2)
+    vision_only = train[:n_unpaired]
+    text_only = train[n_unpaired : 2 * n_unpaired]
+    assert not set(vision_only.tolist()) & set(text_only.tolist())
+
+    paired_n = min(args.paired_train, len(train))
+    paired_train = train[:paired_n]
+
+    val_key = "val" if "val" in by_split else "validation"
+    val = np.asarray(by_split[val_key], dtype=np.int64)[: args.max_eval]
+    test = np.asarray(by_split["test"], dtype=np.int64)[: args.max_eval]
+
+    all_rows = sorted(
+        set(vision_only.tolist())
+        | set(text_only.tolist())
+        | set(paired_train.tolist())
+        | set(val.tolist())
+        | set(test.tolist())
+    )
+    manifest = {
+        "dataset": args.dataset,
+        "dataset_split": DATASET_SPLIT,
+        "seed": args.seed,
+        "vision_only_train": vision_only.tolist(),
+        "text_only_train": text_only.tolist(),
+        "paired_train": paired_train.tolist(),
+        "val": val.tolist(),
+        "test": test.tolist(),
+        "all_rows": all_rows,
+        "protocol": (
+            "vision_only_train and text_only_train contain disjoint image IDs; "
+            "val/test pairs are held out from all bridge training"
+        ),
+    }
+    write_json(args.output, manifest)
+    print(
+        f"Wrote {args.output}: unpaired={n_unpaired}+{n_unpaired}, "
+        f"paired_upper_bound={paired_n}, val={len(val)}, test={len(test)}, "
+        f"features={len(all_rows)} rows"
+    )
+
+
+if __name__ == "__main__":
+    main()
+
-- 
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