LOGicalThought: Logic-Based Ontological Grounding of LLMs for High-Assurance Reasoning

• URL: http://arxiv.org/abs/2510.01530v1
• Date: Thu, 02 Oct 2025 00:06:23 GMT
• Title: LOGicalThought: Logic-Based Ontological Grounding of LLMs for High-Assurance Reasoning
• Authors: Navapat Nananukul, Yue Zhang, Ryan Lee, Eric Boxer, Jonathan May, Vibhav Giridhar Gogate, Jay Pujara, Mayank Kejriwal,
• Abstract summary: High-assurance reasoning requires conclusions that are accurate, verifiable, and grounded in evidence.<n>This paper proposes a novel neurosymbolically-grounded architecture called LOGicalThought.<n>It uses an advanced logical language and reasoner in conjunction with an LLM to construct a dual symbolic graph context and logic-based context.
• Score: 33.30049437667383
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-assurance reasoning, particularly in critical domains such as law and medicine, requires conclusions that are accurate, verifiable, and explicitly grounded in evidence. This reasoning relies on premises codified from rules, statutes, and contracts, inherently involving defeasible or non-monotonic logic due to numerous exceptions, where the introduction of a single fact can invalidate general rules, posing significant challenges. While large language models (LLMs) excel at processing natural language, their capabilities in standard inference tasks do not translate to the rigorous reasoning required over high-assurance text guidelines. Core reasoning challenges within such texts often manifest specific logical structures involving negation, implication, and, most critically, defeasible rules and exceptions. In this paper, we propose a novel neurosymbolically-grounded architecture called LOGicalThought (LogT) that uses an advanced logical language and reasoner in conjunction with an LLM to construct a dual symbolic graph context and logic-based context. These two context representations transform the problem from inference over long-form guidelines into a compact grounded evaluation. Evaluated on four multi-domain benchmarks against four baselines, LogT improves overall performance by 11.84% across all LLMs. Performance improves significantly across all three modes of reasoning: by up to +10.2% on negation, +13.2% on implication, and +5.5% on defeasible reasoning compared to the strongest baseline.

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