Update README.md

README.md

@@ -1,44 +1,82 @@
 ---
-license: mit
+license: cc-by-4.0
 library_name: pytorch
 tags:
   - digital-twins
   - scene-graphs
+  - dynamic-scene-graphs
   - robotics
-  - computer-vision
-  - graph-neural-network
+  - support-relations
+  - graph-maintenance
+  - counterfactual-evidence
+  - pytorch
 ---
 
 # CF-SupportNet
 
-CF-SupportNet is the learned edge-scoring component of **MedPhyGraph**
-(ECCV 2026 TwinWorld Workshop). It is NOT the full MedPhyGraph
-framework by itself: MedPhyGraph also applies deterministic State
-Consistency and Union-Based Transition-Aware Consistency (non-learned,
-code-only) on top of these scores to produce the final maintained
-support graph. See the paper and code at
-https://medphygraph.github.io/ for the complete pipeline.
+CF-SupportNet is the learned edge-scoring component of **MedPhyGraph** (ECCV 2026 TwinWorld Workshop). It is **not** the full MedPhyGraph framework by itself: MedPhyGraph additionally applies deterministic State Consistency and Union-Based Transition-Aware Consistency — non-learned, code-only operations — on top of these scores to produce the final maintained support graph. See the [project page](https://medphygraph.github.io/) for the paper and complete pipeline code.
 
 ## Files
 
-- `health_dyphygraph_r1.0_seed0.pt` — primary checkpoint used for all
-  headline paper results (rho=1.0, seed 0)
-- `health_dyphygraph_r1.0_seed{1-4}.pt` — used only for the paper's
-  multi-seed reproducibility audit; not intended as alternative
-  production checkpoints
+| File | Role |
+|---|---|
+| `health_dyphygraph_r1.0_seed0.pt` | Primary checkpoint — used for all headline results in the paper (ρ=1.0, seed 0) |
+| `health_dyphygraph_r1.0_seed{1-4}.pt` | Used only for the paper's multi-seed reproducibility audit; not intended as alternative production checkpoints |
 
 ## Architecture
 
-One-layer GRU (hidden width 64), fusion MLP, 25,409 trainable
-parameters. Full training details in the paper, Section 3.4.
+- One-layer GRU, hidden width 64 (temporal encoder over counterfactual evidence channels)
+- Geometry branch: 2 → 32 → 32 MLP (static geometric channels)
+- Fusion branch: 96 → 64 → 64 MLP, dropout 0.1
+- **25,409 trainable parameters** total
+
+Full architectural and training details are in the paper, Section 3.4.
+
+## Results
+
+Frozen seed-0 checkpoint, full observation (ρ=1.0):
+
+| Protocol | Transition-Macro Dyn-F1 | Pooled Delta Micro-F1 | Transfer Dyn-F1 |
+|---|---|---|---|
+| Core (15 transfers, Isaac + Procedural) | 1.000 | 1.000 | 1.000 |
+| Expanded (217 transfers, 19 templates) | — | — | 0.998 |
+
+Across seeds 0–4, pooled Transfer Dyn-F1 on the expanded suite is 0.997 ± 0.001.
+
+## Loading the checkpoint
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+
+path = hf_hub_download(
+    repo_id="MedPhyGraph/CF-SupportNet",
+    filename="health_dyphygraph_r1.0_seed0.pt",
+)
+state_dict = torch.load(path, map_location="cpu", weights_only=True)
+```
+
+> This repository hosts raw PyTorch weights only, not a packaged `from_pretrained`-compatible class. To reconstruct the model, define the architecture above (or import it from the [code repository](https://medphygraph.github.io/)) and load `state_dict` into it directly.
+
+## Limitations
+
+- The counterfactual evidence used at both training and inference comes from an analytic AABB-based host-removal proxy, not a full physics simulator — it does not model mesh collisions, contact forces, or materials.
+- Operates downstream of perception on structured object states (poses, categories, AABB extents); it does not take raw RGB-D or perception input.
+- Evaluates direct, primary `SupportedBy` relations only.
+- Assumes the correct destination candidate is present in the candidate set at inference; if it is not, a support transfer cannot be recovered by the scorer or the downstream graph-maintenance stages.
+- Does not model distributed support, articulated contact, or temporary/transient support.
+
+## Related artifacts
+
+- Structured training/evaluation data (procedural + Isaac for Healthcare-derived scenes): `MedPhyGraph/support-graph-data` *(forthcoming)*
 
 ## Citation
 
 ```bibtex
 @inproceedings{gholizadeh2026medphygraph,
-  title={MedPhyGraph: Counterfactual Support-Graph Maintenance for Dynamic Built-Environment Digital Twins},
-  author={Gholizadeh HamlAbadi, Kamran and Vahdati, Monica and El Saddik, Abdulmotaleb},
-  booktitle={ECCV 2026 TwinWorld Workshop},
-  year={2026}
+  title     = {MedPhyGraph: Counterfactual Support-Graph Maintenance for Dynamic Built-Environment Digital Twins},
+  author    = {Gholizadeh HamlAbadi, Kamran and Vahdati, Monica and El Saddik, Abdulmotaleb},
+  booktitle = {ECCV 2026 Workshop on Visual Intelligence for Built Environment Digital Twins (TwinWorld)},
+  year      = {2026}
 }
-```
+```