Modal Logic Neural Networks

Abstract:Neural Networks are indispensable to natural sciences and society. Their impact extends from applications in public health to workforce productivity. Here, we introduce Modal Logic Neural Networks (MLNNs) -- an end-to-end differentiable logical neural network realisation of modal logic which evaluates a learnable truth function across possible-world semantics. This neural architecture handles para-consistency and inconsistency via a learnable world accessibility relation and valuation function. Because the modality is fixed by which frame axioms the relation satisfies rather than by the operator, one differentiable engine covers the epistemic, doxastic, deontic and temporal readings, with applications from verification of reactive and distributed systems to legal discourse and microeconomic utility models. In this paper, we introduce a model of differentiable Kripke semantics, and establish their soundness, convergence, and structural guarantees. We show four applications, in which the learned relation reads as a trust matrix, an operating-regime embedding with safety bounds, a temporal precedence order, and a recovered constraint graph.

Machine Learning (cs.LG); Logic in Computer Science (cs.LO); Multiagent Systems (cs.MA)

arXiv:2512.03491 [cs.LG]

(or arXiv:2512.03491v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2512.03491 Focus to learn more arXiv-issued DOI via DataCite

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