| title | ReasoningLens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models |
|---|---|
| authors | Jun Zhang, Jiasheng Zheng, Boxi Cao, Yaojie Lu, Hongyu Lin, Jia Zheng, Xianpei Han, Le Sun |
| date | 2026-06-22 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.23404 |
| upvotes | 4 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | The emergence of Large Reasoning Models has introduced exceptionally long Chain-of-Thought traces, creating a transparency burden where critical logic is often buried under massive procedural text. To address this, we present ReasoningLens, an open-source framework designed for the hierarchical visualization and diagnostic auditing of complex reasoning chains. ReasoningLens addresses information necropsy by: (1) structuring traces into interactive hierarchies that separate high-level strategy from low-level execution; (2) leveraging an agentic auditor for automated error detection and tool-augmented verification; and (3) synthesizing systemic reasoning profiles to reveal model-specific blind spots. By transforming unstructured walls of text into actionable insights, ReasoningLens provides a modular foundation for interpreting, debugging, and optimizing the next generation of reasoning-centric AI. |
cite
@misc{zhang2026reasoninglens,
title={ReasoningLens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models},
author={Jun Zhang and Jiasheng Zheng and Boxi Cao and Yaojie Lu and Hongyu Lin and Jia Zheng and Xianpei Han and Le Sun},
year={2026},
eprint={2606.23404},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.23404}
}