Large Language Models (LLMs) often exhibit limited logical coherence, mapping premises to conclusions without adherence to explicit inference rules. We propose Proof-Carrying Reasoning with LLMs (PCRLLM), a framework that constrains reasoning to single-step inferences while preserving natural language formulations. Each output explicitly specifies premises, rules, and conclusions, thereby enabling verification against a target logic. This mechanism mitigates trustworthiness concerns by supporting chain-level validation even in black-box settings. Moreover, PCRLLM facilitates systematic multi-LLM collaboration, allowing intermediate steps to be compared and integrated under formal rules. Finally, we introduce a benchmark schema for generating large-scale step-level reasoning data, combining natural language expressiveness with formal rigor.
@article{arxiv.2511.08392,
title = {PCRLLM: Proof-Carrying Reasoning with Large Language Models under Stepwise Logical Constraints},
author = {Tangrui Li and Pei Wang and Hongzheng Wang Christian Hahm and Matteo Spatola and Justin Shi},
journal= {arXiv preprint arXiv:2511.08392},
year = {2025}
}