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        <title>ReasonGraph: Visualization of Reasoning Methods and Extended Inference Paths</title>
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    <abstract>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 and extended inference outputs 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 excellent usability across various downstream applications. By providing a unified visualization framework, ReasonGraph reduces cognitive load in analyzing complex reasoning paths, improves error identification in logical processes, and enables more effective development of LLM-based applications. The platform is open-source, facilitating accessibility and reproducibility in LLM reasoning analysis.</abstract>
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%0 Conference Proceedings
%T ReasonGraph: Visualization of Reasoning Methods and Extended Inference Paths
%A Li, Zongqian
%A Shareghi, Ehsan
%A Collier, Nigel
%Y Mishra, Pushkar
%Y Muresan, Smaranda
%Y Yu, Tao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-253-4
%F li-etal-2025-reasongraph
%X 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 and extended inference outputs 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 excellent usability across various downstream applications. By providing a unified visualization framework, ReasonGraph reduces cognitive load in analyzing complex reasoning paths, improves error identification in logical processes, and enables more effective development of LLM-based applications. The platform is open-source, facilitating accessibility and reproducibility in LLM reasoning analysis.
%R 10.18653/v1/2025.acl-demo.14
%U https://aclanthology.org/2025.acl-demo.14/
%U https://doi.org/10.18653/v1/2025.acl-demo.14
%P 140-147

Markdown (Informal)

[ReasonGraph: Visualization of Reasoning Methods and Extended Inference Paths](https://aclanthology.org/2025.acl-demo.14/) (Li et al., ACL 2025)

• ReasonGraph: Visualization of Reasoning Methods and Extended Inference Paths (Li et al., ACL 2025)

ACL

• Zongqian Li, Ehsan Shareghi, and Nigel Collier. 2025. ReasonGraph: Visualization of Reasoning Methods and Extended Inference Paths. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 140–147, Vienna, Austria. Association for Computational Linguistics.