TopoRefine: Iterative Refinement with Reasoning Topology as High-Level Feedback

Abstract

By leveraging effective signals to refine their outputs, large language models (LLMs) can achieve superior performance compared to single-pass outputs. However, internal signals often suffer from accumulated hallucinations and a lack of confidence, while external signals are typically difficult to obtain and apply, hindering the development of the self-refinement paradigm. In this work, we propose a novel method named TopoRefine, which integrates the reasoning topology within the model’s outputs as reliable high-level feedback. Specifically, TopoRefine encourages LLMs to explore reasoning paths without assuming a predefined direction for improvement, while using a consistency mechanism to prevent performance degradation. Reasoning topology can provide higher-level information as feedback, offering more precise semantics and facilitating self-analysis. Experiments on mathematical datasets and with various LLMs demonstrate significant improvements using our method. We also provide a detailed analysis of TopoRefine’s efficiency.