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ReasonGraph: Visualisation of Reasoning Paths

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arxiv 2503.03979 v1 pith:P7XLHCUX submitted 2025-03-06 cs.CL cs.AIcs.HC

classification cs.CLcs.AIcs.HC
keywords reasoningreasongraphprocessesvisualizationanalyzingapplicationsefficientframework
verification ladder T0 review T1 audit T2 compute T3 formal
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Large Language Models (LLMs) reasoning processes are challenging to analyze due to their complexity and the lack of organized visualization tools. We present ReasonGraph, a web-based platform for visualizing and analyzing LLM reasoning processes. It supports both sequential and tree-based reasoning methods while integrating with major LLM providers and over fifty state-of-the-art models. ReasonGraph incorporates an intuitive UI with meta reasoning method selection, configurable visualization parameters, and a modular framework that facilitates efficient extension. Our evaluation shows high parsing reliability, efficient processing, and strong usability across various downstream applications. By providing a unified visualization framework, ReasonGraph reduces cognitive load in analyzing complex reasoning paths, improves error detection in logical processes, and enables more effective development of LLM-based applications. The platform is open-source, promoting accessibility and reproducibility in LLM reasoning analysis.

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Cited by 1 Pith paper

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  1. A Survey on Prompt Tuning

    cs.CL 2025-07 conditional novelty 4.0 of 10

    A survey that categorizes prompt tuning methods into direct and transfer learning branches, describes each method's design and limitations, and outlines challenges and future work.

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