PCRLLM: Proof-Carrying Reasoning with Large Language Models under Stepwise Logical Constraints
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 12 of its 19 references that are in the map. First posted on arXiv 11 Nov 2025.
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- 2025Are LLMs Stable Formal Logic Translators in Logical Reasoning Across Linguistically Diversified Texts?5 Jun
- 2023Improving Factuality and Reasoning in Language Models through Multiagent Debate23 May
- 2023Using Language Models For Knowledge Acquisition in Natural Language Reasoning Problems4 Apr
Other references · 9
- 2021Training Verifiers to Solve Math Word Problemscited by 9,743
- 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
- 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
- 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,057
- 2024To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoningcited by 257
- 2023Let's reward step by step: Step-Level reward model as the Navigators for Reasoningcited by 96
- 2022A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directionscited by 51
- 2024Diversity of Thought Elicits Stronger Reasoning Capabilities in Multi-Agent Debate Frameworkscited by 12
- 2025Thoughts without Thinking: Reconsidering the Explanatory Value of Chain-of-Thought Reasoning in LLMs through Agentic Pipelinescited by 7
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- 2024Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning