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>

tests/test_fusion_contract.py

@@ -0,0 +1,60 @@
+from __future__ import annotations
+
+"""Contract tests shared by all fusion implementations."""
+
+import torch
+
+from fuse_proj.data.types import FusionViewOutput, ViewBatch, ViewName
+from fuse_proj.models.fusion.base import FusionModelBase
+
+
+class DummyFusion(FusionModelBase):
+    """Minimal parameter-free implementation used only to test the base contract."""
+
+    def encode_view(self, batch: ViewBatch, view: ViewName) -> FusionViewOutput:
+        """Convert RGB channel means into a deterministic normalized descriptor."""
+        del view
+        rgb_summary = batch["rgb"].mean(dim=(-2, -1))  # [B, 3]
+        repeats = (self.descriptor_dim + rgb_summary.shape[1] - 1) // rgb_summary.shape[1]
+        raw = rgb_summary.repeat(1, repeats)[:, : self.descriptor_dim]  # [B, D]
+        descriptor = self.normalize_descriptor(raw)
+        contributions = torch.zeros(raw.shape[0], 3, dtype=raw.dtype, device=raw.device)
+        return {
+            "descriptor": descriptor,
+            "rgb_descriptor": descriptor,
+            "modality_contributions": contributions,
+            "diagnostics": {},
+        }
+
+
+def _make_batch(batch_size: int) -> ViewBatch:
+    """Create a valid synthetic multimodal batch."""
+    return {
+        "rgb": torch.rand(batch_size, 3, 256, 256),
+        "geometry": torch.rand(batch_size, 1, 256, 256),
+        "segmentation": torch.zeros(batch_size, 1, 256, 256, dtype=torch.uint8),
+        "geometry_valid": torch.ones(batch_size, 1, 256, 256, dtype=torch.bool),
+        "segmentation_valid": torch.ones(batch_size, 1, 256, 256, dtype=torch.bool),
+        "text_valid": torch.ones(batch_size, dtype=torch.bool),
+        "text": ["urban scene"] * batch_size,
+        "sample_id": [f"sample-{index}" for index in range(batch_size)],
+    }
+
+
+def test_pair_output_shapes() -> None:
+    """The base contract must return one normalized descriptor per view."""
+    model = DummyFusion(descriptor_dim=1024)
+    output = model(_make_batch(4), _make_batch(4))
+
+    for view in ("satellite", "uav"):
+        descriptor = output[view]["descriptor"]
+        assert descriptor.shape == (4, 1024)
+        assert torch.allclose(descriptor.norm(dim=-1), torch.ones(4), atol=1e-5)
+
+
+def test_batch_size_one() -> None:
+    """Fusion implementations must not squeeze away the batch dimension."""
+    model = DummyFusion(descriptor_dim=1024)
+    output = model(_make_batch(1), _make_batch(1))
+    assert output["satellite"]["descriptor"].shape == (1, 1024)
+    assert output["uav"]["descriptor"].shape == (1, 1024)