Adaptive Stopping for Multi-Turn LLM Reasoning

A recent paper on arXiv (2604.01413v3) tackles the issue of when to halt large language models (LLMs) in multi-turn reasoning frameworks, including adaptive retrieval-augmented generation (RAG) and ReAct-style agents. These techniques enhance precision by iteratively retrieving data, reasoning, or taking action. However, current stopping criteria are either heuristic or based on fixed turn limits, lacking formal assurance that the ultimate prediction is accurate. This poses significant challenges in critical areas like finance and healthcare, where excessive turns can lead to increased costs and delays, while premature stops may result in erroneous choices. The authors propose a solution utilizing conformal prediction (CP), which offers formal coverage guarantees, yet existing LLM-CP approaches are limited to single model outputs and do not accommodate multi-turn pipelines with adaptive stopping. They aim to address this shortcoming, although the abstract ends before elaborating on their methodology. The paper can be accessed on arXiv and was introduced as a replace-cross type.