[2510.04116] Searching Meta Reasoning Skeleton to Guide LLM Reasoning

Computer Science > Artificial Intelligence arXiv:2510.04116 (cs) [Submitted on 5 Oct 2025 (v1), last revised 28 Mar 2026 (this version, v3)] Title:Searching Meta Reasoning Skeleton to Guide LLM Reasoning Authors:Ziying Zhang, Yaqing Wang, Quanming Yao View a PDF of the paper titled Searching Meta Reasoning Skeleton to Guide LLM Reasoning, by Ziying Zhang and 2 other authors View PDF HTML (experimental) Abstract:Meta reasoning behaviors work as a skeleton to guide large language model (LLM) reasoning, thus help to improve reasoning performance. However, prior researches implement meta reasoning skeleton with manually designed structure, limiting ability to adapt to query-specific requirement and capture intricate logical dependency among reasoning steps. To deal with the challenges, we represent meta reasoning skeleton with directed acyclic graph (DAG) to unify skeletons proposed in prior works and model intricate logical dependency. Then we propose AutoMR, a framework that searches for query-aware meta reasoning skeleton automatically inspired by automated machine learning (AutoML). Specifically, we construct search space based on DAG representation of skeleton and then formulate the search problem. We design a dynamic skeleton sampling algorithm by expanding meta reasoning skeleton along with reasoning context at inference time. This algorithm can derive any meta reasoning skeleton in search space efficiently and adapt skeleton to evolving base reasoning context, thus ena...