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 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
arXiv AI
Jul 24

Representation Robustness Under Executable Reasoning Constraints in Large Language Models for Mathematical Problem Solving

arXiv:2607. 20520v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated on mathematical problem solving, yet prior work often treats representationally equivalent formulations as interchangeable and conflates reasoning errors with interface failures.

By Sagnik Nath, Edith Aurora Graf, Liang Zhang, Diego Zapata-Rivera