Benchmarks

Datasets, tasks, and protocols for measuring and comparing models.

Topological Deep Learning Challenge 2026: Bridging the Gap

TAG-DS 2026

The 2026 Topological Deep Learning Challenge put graph neural networks and higher-order topological neural networks side by side in TopoBench, on synthetic graphs whose structure is set by design, and found that which family does better depends on the task. Our implementation of the directed sheaf neural network took first place in the graph neural network track.

Guillermo Bernárdez, Lev Telyatnikov, Mathilde Papillon, Marco Montagna, Louisa Cornelis, Louis Van Langendonck, ..., Dario Loi, ..., Nina Miolane

PMLR arXiv DSNN code BibTeX

Topological Deep Learning Challenge 2025: Expanding the Data Landscape

TAG-DS 2025

The 2025 Topological Deep Learning Challenge asked the community to extend TopoBench with new datasets, benchmark tasks, and data pipelines; this report describes the challenge and what it received.

Guillermo Bernárdez, Lev Telyatnikov, Mathilde Papillon, Marco Montagna, Raffael Theiler, Louisa Cornelis, ..., Dario Loi, ..., Nina Miolane

PMLR PDF TopoBench BibTeX

AutoBench: Automating LLM Evaluation through Reciprocal Peer Assessment

Preprint, 2025

Language models take turns writing tasks, answering them, and judging each other's answers; weighting each judge by how reliable it proves yields rankings that agree with established benchmarks.

Dario Loi, Elena Maria Muià, Federico Siciliano, Giovanni Trappolini, Vincenzo Crisà, Peter Kruger, Fabrizio Silvestri

arXiv AutoBench BibTeX

Papers on benchmarks

Topological Deep Learning Challenge 2026: Bridging the Gap

Topological Deep Learning Challenge 2025: Expanding the Data Landscape

AutoBench: Automating LLM Evaluation through Reciprocal Peer Assessment