# ReasonGraph: Visualisation of Reasoning Paths

arXiv: https://arxiv.org/abs/2503.03979 · HTML: https://mlsanity.com/paper/2503.03979/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 3 of 3)

- [Prompt Compression for Large Language Models: A Survey](https://mlsanity.com/paper/2410.12388/) (2024)
- [500xCompressor: Generalized Prompt Compression for Large Language Models](https://mlsanity.com/paper/2408.03094/) (2024)
- [Visualizing Attention in Transformer-Based Language Representation Models](https://mlsanity.com/paper/1904.02679/) (2019)

## Built on by (most cited first, 6 of 6)

- [Characterizing, Evaluating, and Optimizing Complex Reasoning](https://mlsanity.com/paper/2602.08498/) (2026)
- [GraphLocator: Graph-guided Causal Reasoning for Issue Localization](https://mlsanity.com/paper/2512.22469/) (2025)
- [ReTrace: Interactive Visualizations for Reasoning Traces of Large Reasoning Models](https://mlsanity.com/paper/2511.11187/) (2025)
- ["I May Not Have Articulated Myself Clearly": Diagnosing Dynamic Instability in LLM Reasoning at Inference Time](https://mlsanity.com/paper/2602.02863/) (2026)
- [ReasoningLens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models](https://mlsanity.com/paper/2606.23404/) (2026)
- [An Interactive Paradigm for Deep Research](https://mlsanity.com/paper/2605.24266/) (2026)

## Abstract (arXiv)

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.