---
license: mit
library_name: pytorch
tags:
  - digital-twins
  - scene-graphs
  - robotics
  - computer-vision
  - graph-neural-network
---

# 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.

## 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

## Architecture

One-layer GRU (hidden width 64), fusion MLP, 25,409 trainable
parameters. Full training details in the paper, Section 3.4.

## 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}
}
```