arXiv AI By Ramya Keerthy Thatikonda, Wray Buntine, Ehsan Shareghi

Beyond Surface Forms: Symbolic Edits as a Test for Logical Reasoning with LLMs

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arXiv AI
Oct 23

When Form Changes but Logic Doesn't: Building Logic-invariant LLMs through Logical Structure

The paper investigates whether large language models (LLMs) truly grasp logical structure or merely rely on surface semantics. By evaluating models on logically equivalent inputs, the authors find that many LLMs fail to produce consistent predictions and struggle with unseen logical forms. They introduce LoGIST, a framework that maps inputs to decision diagrams and uses these logical embeddings to guide LLM reasoning, thereby reducing inconsistency and improving generalization.

By Xuyuan Liu, Xinshuai Dong, Elynn Chen, Yujun Yan
arXiv Computation and Language
Aug 28

Neuro-symbolic PRM: Enhancing Scientific Reasoning via Structured Traces and Symbolic Verification

The paper introduces a neuro‑symbolic framework for scientific reasoning that separates symbolic validity and semantic groundedness. A deterministic symbolic verifier acts as a hard filter to guarantee syntactic and arithmetic correctness, while a Process Reward Model (PRM) is trained on verifier‑accepted steps to assess contextual grounding. The authors propose Counterfactual Symbolic Perturbation (CSP) to generate hard negative examples that pass the verifier but are logically flawed, enabling efficient PRM training and a verifier‑first constrained search at inference.

By Yuxin Zi, Cong Xu, Suparna Bhattacharya, Martin Foltin, Amit Sheth