cut elimination 1differentiable loss functions 1neuro-symbolic integration 1quantitative linear logic 1soft residuated lattices 1
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cs.LO2026
Adequate Losses via Quantitative Linear Logic
Matteo Capucci, Robert Atkey, Charles Grellois +2
The paper proposes a family of quantitative linear logics (pQLL) that combine logical specifications with differentiable loss functions for neural components, proving cut‑eliminati…
cs.LO2026
Quantitative Linear Logic for Neuro-Symbolic Learning and Verification
Thomas Flinkow, Ekaterina Komendantskaya, Matteo Capucci +1
Differentiable Logics are deployed in neuro-symbolic learning tasks as a way of embedding logical constraints in the training objective of neural networks. A differentiable logic c…
cs.LO2026
Compositionality of Lyapunov functions via assume-guarantee reasoning
Matteo Capucci, David Jaz Myers
Assume-guarantee reasoning is a technique for compositional model checking in which system specifications are checked under certain assumptions on system parameters or inputs, and…