AILOJun 16

A homotopy-type-theoretic generalization of neurosymbolic inference

arXiv:2606.1785110.9Has Code
Predicted impact top 62% in AI · last 90 daysOriginality Highly original
AI Analysis

For researchers in neurosymbolic AI, this work provides a principled framework to handle reasoning shortcuts, improving calibration without sacrificing accuracy.

The paper generalizes neurosymbolic inference using homotopy type theory to account for symmetries in logical structures and multiple proofs. This yields a shortcut-aware concept posterior that, on MNIST reasoning-shortcut benchmarks, is better calibrated than diversity-trained ensembles while maintaining label accuracy.

A wide range of neurosymbolic (NeSy) systems compute one functional: a belief-weighted sum of a logical quantity over a space of $σ$-structures, of which weighted model counting, fuzzy logic, and probabilistic logic are special cases. This account is built on sets, and a set deliberately forgets two things that are important for NeSy: when two $σ$-structures are the same up to a symmetry of the theory, and how many distinct proofs witness a query. Replacing the underlying sets by types, in the sense of homotopy type theory, preserves this information, and turns this functional into a belief-weighted homotopy cardinality, a notion of size that counts each object in inverse proportion to its symmetries. We develop the framework from scratch for NeSy systems, prove a conservativity theorem that recovers the classical functional when symmetries are trivial, and show that the symmetry our framework exposes is exactly the one behind reasoning shortcuts. The payoff is concrete: the shortcut-aware concept posterior that recent methods reach by ensembling or expressive density estimation is the only symmetry-invariant point of the confusion-set simplex, computable in closed form by averaging a single model over the symmetry group. On MNIST reasoning-shortcut benchmarks this single-model wrapper is better calibrated than a diversity-trained ensemble, while leaving label accuracy and identifiable concepts untouched. Code is freely available at https://github.com/bio-ontology-research-group/hott-nesy.

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