[2510.08992] Constraints-of-Thought: A Framework for Constrained Reasoning in Language-Model-Guided Search

Computer Science > Machine Learning arXiv:2510.08992 (cs) [Submitted on 10 Oct 2025 (v1), last revised 27 Mar 2026 (this version, v2)] Title:Constraints-of-Thought: A Framework for Constrained Reasoning in Language-Model-Guided Search Authors:Kamel Alrashedy, Vriksha Srihari, Zulfiqar Zaidi, Ridam Srivastava, Pradyumna Tambwekar, Matthew Gombolay View a PDF of the paper titled Constraints-of-Thought: A Framework for Constrained Reasoning in Language-Model-Guided Search, by Kamel Alrashedy and 5 other authors View PDF HTML (experimental) Abstract:While researchers have made significant progress in enabling large language models (LLMs) to perform multi-step planning, LLMs struggle to ensure that those plans align with high-level user intent and satisfy symbolic constraints, especially in complex, multi-step domains. Existing reasoning approaches such as Chain-of-Thought (CoT), Tree-of-Thought (ToT), and verifier-augmented methods, expand the search space but often yield infeasible actions or hallucinated steps. To overcome these limitations, we propose Constraints-of-Thought (Const-o-T), a framework that provides a structured prior that enables Monte Carlo Tree Search (MCTS) focus search on semantically meaningful paths. Each reasoning step is represented as an (intent, constraint) pair, which serves both to compress the search space and enforce validity. Unlike prior methods that merely generate reasoning traces or validate outputs post hoc, Const-o-T uses (intent, constrai...