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+# Track 2 — SheafHyperGNN Submission
+
+## Track
+
+Track 2 — Topological Neural Networks (TNNs)
+
+## Team Name
+
+s/pairwise/ho
+
+## Model
+
+Sheaf Hypergraph Networks (SheafHyperGNN)
+
+## Status
+
+Draft / work in progress
+
+## Summary
+
+This draft PR develops a TopoBench-native implementation of SheafHyperGNN from
+"Sheaf Hypergraph Networks" (Duta et al., NeurIPS 2023) for the 2026 TDL
+Challenge.
+
+The model generalises standard hypergraph convolutions by equipping every node
+and hyperedge with a *d*-dimensional stalk and learning per-pair restriction
+maps that define a cellular sheaf over the hypergraph. The resulting sheaf
+Laplacian replaces the standard hypergraph Laplacian in the diffusion operator,
+enabling richer structural representations without additional data requirements
+beyond the incidence matrix.
+
+## Planned Implementation
+
+- [x] Inspect official implementation and paper equations; confirm feasibility.
+- [ ] Add SheafHyperGNN backbone under `topobench/nn/backbones/hypergraph/sheaf_hypergnn.py`.
+- [ ] Add Hydra config under `configs/model/hypergraph/sheaf_hypergnn.yaml`.
+- [ ] Add unit tests.
+- [ ] Update `test/pipeline/test_pipeline.py`.
+- [ ] Run the official GraphUniverse evaluation notebook.
+- [ ] Add generated `results.json`.
+
+## Reference
+
+Duta, I., Cassarà, G., Silvestri, F., & Liò, P.
+"Sheaf Hypergraph Networks." *NeurIPS 2023.*
+
+Paper: https://arxiv.org/abs/2309.17116
+
+Official implementation: https://github.com/IuliaDuta/sheaf_HNN