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cs.LG2026

Topological Flow Matching

Kacper Wyrwal, İsmail İlkan Ceylan, Alexander Tong

Flow matching is a powerful generative modeling framework, valued for its simplicity and strong empirical performance. However, its standard formulation treats signals on structure…

cs.LG2026

What are the Right Symmetries for Formal Theorem Proving?

Krzysztof Olejniczak, Radoslav Dimitrov, Xingyue Huang +3

Formal theorem provers based on large language models (LLMs) are highly sensitive to superficial variations in problem representation: semantically equivalent statements can exhibi…

cs.AI2026

MUX: Continuous Reasoning via Multiplexed Tokens

Ayhan Suleymanzade, Halil Alperen Gozeten, Michael Bronstein +2

Language models solve complex problems by articulating intermediate reasoning steps in natural language. While effective, this process is computationally bottlenecked: each reasoni…

cs.LG2026

HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs

Xingyue Huang, Mikhail Galkin, Michael M. Bronstein +1

Inductive link prediction with knowledge hypergraphs is the task of predicting missing hyperedges involving completely novel entities (i.e., nodes unseen during training). Existing…

cs.LG2026

MacroGuide: Topological Guidance for Macrocycle Generation

Alicja Maksymiuk, Alexandre Duplessis, Michael Bronstein +3

Macrocycles are ring-shaped molecules that offer a promising alternative to small-molecule drugs due to their enhanced selectivity and binding affinity against difficult targets. D…