fuse_proj: Initial operational package for 3 researchers (Pavlenko/Blizno/Moroz)

Multimodal fusion research on StripNet+GTA-UAV proxy:
- 3 independent fusion tracks: condition-aware (A), token/bottleneck (B), role-aware (C)
- Shared interfaces, protocol, dataset audit, baseline benchmarks
- Canonical version-chain references to vault (SPEC, ANALYSIS, TRIAGE)
- Personalized task plans and decision tables for each researcher
- 3 generated DOCX task assignment files with milestones and DoD checklist
- Full modality dropout diagnostics and missing-modality robustness requirements
- Data contract, benchmark registry, experiment tracking infrastructure

Operational documents:
- docs/00_project/: MERIDIAN context, protocol, repository reuse guide, experiment specification
- docs/01_tasks/: Master assignment + 3 individual researcher tracks + joint integration
- docs/02_references/: Core literature, version-chain bases, code maps
- docs/03_codebase_guides/: Existing code snapshots from vault
- scripts/: gen_task_plans.js (DOCX generation), placeholder infrastructure
- vendor_reference/: Snapshots of caption_test, depth_edges_annotate, existing SOFIA/SegModel code
- reports/, results/, experiments/: Shared output structure for all 3 researchers

3 DOCX files generated from gen_task_plans.js (Times New Roman 14pt, GOST format):
- План_заданий_Павленко_БВ.docx (Condition-Aware track, fusion API owner)
- План_заданий_Близно_МВ.docx (Token/Bottleneck track, benchmark owner)
- План_заданий_Мороз_ЕС.docx (Role-Aware track, data contract owner)

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>

vendor_reference/caption_test/scripts/make_split.py

@@ -0,0 +1,87 @@
+from __future__ import annotations
+
+"""Create 80/20 train/test split from GTA-UAV-LR pair JSONs.
+
+Merges cross-area train+test (33,708 pairs), shuffles deterministically,
+and saves new 80/20 split JSONs.
+
+Usage:
+    python -m scripts.make_split [--ratio 0.8] [--seed 42]
+"""
+
+import argparse
+import json
+import logging
+import random
+from pathlib import Path
+
+import coloredlogs
+
+LOGGER = logging.getLogger("caption_test.make_split")
+
+_RGB_ROOT = Path("/home/servml/Документы/datasets/GTA-UAV-LR")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Create 80/20 split for GTA-UAV-LR.")
+    parser.add_argument("--ratio", type=float, default=0.8, help="Train ratio (default 0.8).")
+    parser.add_argument("--seed", type=int, default=42, help="Random seed.")
+    parser.add_argument(
+        "--output-dir", type=str, default="meta",
+        help="Output directory for split JSONs.",
+    )
+    args = parser.parse_args()
+
+    coloredlogs.install(
+        level="INFO", logger=LOGGER,
+        fmt="%(asctime)s %(name)s %(levelname)s %(message)s",
+    )
+
+    # Load both original splits.
+    train_path = _RGB_ROOT / "cross-area-drone2sate-train.json"
+    test_path = _RGB_ROOT / "cross-area-drone2sate-test.json"
+
+    LOGGER.info("📂 Loading %s", train_path.name)
+    with open(train_path) as f:
+        part1 = json.load(f)
+    LOGGER.info("📂 Loading %s", test_path.name)
+    with open(test_path) as f:
+        part2 = json.load(f)
+
+    all_pairs = part1 + part2
+    LOGGER.info("📊 Total pairs: %d", len(all_pairs))
+
+    # Shuffle deterministically.
+    rng = random.Random(args.seed)
+    rng.shuffle(all_pairs)
+
+    # Split.
+    n_train = int(len(all_pairs) * args.ratio)
+    train_pairs = all_pairs[:n_train]
+    test_pairs = all_pairs[n_train:]
+
+    LOGGER.info(
+        "✂️  Split %.0f/%.0f: train=%d (%.1f%%)  test=%d (%.1f%%)",
+        args.ratio * 100, (1 - args.ratio) * 100,
+        len(train_pairs), 100 * len(train_pairs) / len(all_pairs),
+        len(test_pairs), 100 * len(test_pairs) / len(all_pairs),
+    )
+
+    # Save.
+    out_dir = Path(args.output_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    train_out = out_dir / "train_80.json"
+    test_out = out_dir / "test_20.json"
+
+    with train_out.open("w", encoding="utf-8") as f:
+        json.dump(train_pairs, f)
+    with test_out.open("w", encoding="utf-8") as f:
+        json.dump(test_pairs, f)
+
+    LOGGER.info("💾 Saved: %s (%d pairs)", train_out, len(train_pairs))
+    LOGGER.info("💾 Saved: %s (%d pairs)", test_out, len(test_pairs))
+
+
+if __name__ == "__main__":
+    main()