QLL is a novel logic for neuro-symbolic learning that uses ML-native operations (sum, log-sum-exp) on logits to embed constraints, satisfying most linear logic properties and showing stronger correlation between empirical robustness and formal verification than prior approaches.
An Algebraic Investigation of
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The authors perform and analyze three reformalizations of the Jordan Curve Theorem from Mizar to Lean, HOL Light to Lean, and HOL Light to Agda.
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Quantitative Linear Logic for Neuro-Symbolic Learning and Verification
QLL is a novel logic for neuro-symbolic learning that uses ML-native operations (sum, log-sum-exp) on logits to embed constraints, satisfying most linear logic properties and showing stronger correlation between empirical robustness and formal verification than prior approaches.
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Reformalization of the Jordan Curve Theorem
The authors perform and analyze three reformalizations of the Jordan Curve Theorem from Mizar to Lean, HOL Light to Lean, and HOL Light to Agda.