Teaching LLMs to Ask: Self-Querying Category-Theoretic Planning for Under-Specified Reasoning скачать в хорошем качестве

Teaching LLMs to Ask: Self-Querying Category-Theoretic Planning for Under-Specified Reasoning

Large language models often struggle to generate feasible plans when task descriptions are incomplete, frequently making incorrect assumptions or violating constraints due to missing information. To address this challenge, researchers introduced Self-Querying Bidirectional Categorical Planning (SQ-BCP), a framework that explicitly tracks the status of action preconditions as satisfied, violated, or unknown rather than silently filling in gaps. The system resolves uncertainty through a deterministic refinement policy that either queries a user for specific facts or constructs intermediate bridging actions to establish necessary conditions before proceeding. Unlike standard approaches that rely on similarity scores for acceptance, SQ-BCP integrates these refined actions into a bidirectional search process that validates plans using rigorous categorical verification and hard-constraint checks to ensure they are logically and physically executable. Empirical evaluations on WikiHow and RecipeNLG benchmarks demonstrate that this method significantly improves reliability, reducing resource violation rates to less than 15 percent while maintaining competitive quality compared to existing planning baselines. https://arxiv.org/pdf/2601.20014