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GradSTL: Comprehensive Signal Temporal Logic for Neurosymbolic Reasoning and Learning

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arxiv 2508.04438 v1 pith:G2SKZ7XZ submitted 2025-08-06 cs.LO

GradSTL: Comprehensive Signal Temporal Logic for Neurosymbolic Reasoning and Learning

Mark Chevallier , Filip Smola , Richard Schmoetten , Jacques D. Fleuriot This is my paper
classification cs.LO
keywords signalgradstlimplementationlearninglogicneurosymbolictemporalapproach
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present GradSTL, the first fully comprehensive implementation of signal temporal logic (STL) suitable for integration with neurosymbolic learning. In particular, GradSTL can successfully evaluate any STL constraint over any signal, regardless of how it is sampled. Our formally verified approach specifies smooth STL semantics over tensors, with formal proofs of soundness and of correctness of its derivative function. Our implementation is generated automatically from this formalisation, without manual coding, guaranteeing correctness by construction. We show via a case study that using our implementation, a neurosymbolic process learns to satisfy a pre-specified STL constraint. Our approach offers a highly rigorous foundation for integrating signal temporal logic and learning by gradient descent.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Quantitative Linear Logic for Neuro-Symbolic Learning and Verification

    cs.LO 2026-05 unverdicted novelty 7.0

    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 empir...

  2. Quantitative Linear Logic for Neuro-Symbolic Learning and Verification

    cs.LO 2026-05 unverdicted novelty 6.0

    Quantitative Linear Logic interprets logical connectives via natural ML operations on logits to embed constraints in neural training while satisfying most linear logic laws and correlating performance with independent...