mlsanityData to 14 Oct 2026

GradSTL: Comprehensive Signal Temporal Logic for Neurosymbolic Reasoning and Learning

arXiv 2508.04438 ↗First posted 6 Aug 2025cs.LOcited by 1

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 3 of its 23 references that are in the map. First posted on arXiv 6 Aug 2025.

Builds on

3 of 23 references.

References · 3

  1. 2021BarrierNet: A Safety-Guaranteed Layer for Neural Networkscited by 12
  2. 2024TLINet: Differentiable Neural Network Temporal Logic Inferencecited by 9
  3. 2025Formally Verified Neurosymbolic Trajectory Learning via Tensor-based Linear Temporal Logic on Finite Tracescited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Quantitative Linear Logic for Neuro-Symbolic Learning and Verificationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Distilling Formal Logic into Neural Spaces: A Kernel Alignment Approach for Signal Temporal Logic
  2. 2026Time to Reason: Scalable Neurosymbolic Learning for LTLf via Fuzzy Semantics
  3. 2026NeuroNL2LTL: A Neurosymbolic Framework for Natural Language Translation of Linear Temporal Logic
  4. 2025Temporalizing Confidence: Evaluation of Chain-of-Thought Reasoning with Signal Temporal Logic
  5. 2023Scallop: A Language for Neurosymbolic Programming
  6. 2025A Fully Spectral Neuro-Symbolic Reasoning Architecture with Graph Signal Processing as the Computational Backbone
  7. 2025Current Practices for Building LLM-Powered Reasoning Tools Are Ad Hoc -- and We Can Do Better
  8. 2024Neuro-symbolic Learning Yielding Logical Constraints