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Track: Track1; Team name: yeahkitory; Model: Bundle Neural Networks (BuNN)#319

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yeahkitoryisgood:challenge-2026-bunn
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Track: Track1; Team name: yeahkitory; Model: Bundle Neural Networks (BuNN)#319
yeahkitoryisgood wants to merge 22 commits into
geometric-intelligence:mainfrom
yeahkitoryisgood:challenge-2026-bunn

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@yeahkitoryisgood yeahkitoryisgood commented May 16, 2026

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Summary

  • Adds a graph BuNN backbone for the challenge track with learned O(2) bundle maps, random-walk heat-kernel Taylor diffusion, residual/norm/dropout channel updates, and a real Hydra config.
  • Review-driven hardening covers tensor shapes, tensor input types, floating node-feature tensors, node-feature widths, node ids, edge-index device placement, bundle shape constraints, scalar/integer/probability hyperparameters, boolean flags, activation config values, finite real-valued non-negative edge weights, non-contiguous edge-weight tensors, boolean edge-weight rejection, unexpected constructor/forward keyword arguments, TopoBench wrapper output-channel introspection, and weighted random-walk Laplacian coverage.
  • The full challenge result artifact remains tracked at 2026_tdl_challenge/outputs/bunn_full/results.json.

Validation

  • .venv\Scripts\python.exe -m ruff check topobench\nn\backbones\graph\bunn.py test\nn\backbones\graph\test_bunn.py -> passed
  • .venv\Scripts\python.exe -m pytest test\nn\backbones\graph\test_bunn.py -q -> 59 passed
  • .venv\Scripts\python.exe -m pytest test\nn\backbones\graph\test_bunn.py test\pipeline\test_pipeline.py -q -> 60 passed
  • Full challenge grid: 72 runs, community accuracy mean 0.1034, triangle normalized MSE 6.7543

The full grid result above was generated before the invalid-input validation commits; the valid-input computation path is unchanged by these hardening patches.

Submission Info

  • Track: Track1
  • Team name: yeahkitory
  • Model: Bundle Neural Networks (BuNN)

Prasanna28Devadiga pushed a commit to Prasanna28Devadiga/TopoBench that referenced this pull request May 17, 2026
Add the artefacts produced by running 2026_tdl_challenge/run_evaluation.ipynb
with MODEL_CONFIG="graph/conn_nsd":

  outputs/conn_nsd_full/
    results.json                                — 72 rows, 3 seeds × 12 cells × 2 tasks
    heatmap_community_detection_accuracy.png    — accuracy mean ± std heatmap
    heatmap_triangle_mse_over_triangles.png     — MSE / Σtriangles heatmap
    OOD/                                        — 6 OOD-delta panels
      OOD_{low,mid,high}_homophily__{community_detection,triangle_counting}.png

Convention mirrors the BuNN submission (PR geometric-intelligence#319), which uses
2026_tdl_challenge/outputs/bunn_full/. Files are force-added past the
.gitignore rule for the broader outputs/ directory.

Headline numbers (10-epoch budget; the spec explicitly does NOT reward
final performance):
  - Community detection: mean test accuracy 0.075 across all 36 runs;
    chance is 1/12 for the 12-community task. Per-seed range [0.04, 0.12].
  - Triangle counting: mean test MSE / Σtriangles = 10.46 across all
    36 runs.

These confirm the pipeline runs end-to-end on every grid cell with no
NaNs in metric fields that should be finite.
@LouisVanLangendonck LouisVanLangendonck added the track-1-gnn 2026 Topological Deep Learning Challenge -- Track 1 GNNs label May 26, 2026
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3 participants