@inproceedings{zhang-etal-2026-reasoninglens,
title = "{R}easoning{L}ens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models",
author = "Zhang, Jun and
Zheng, Jiasheng and
Cao, Boxi and
Lu, Yaojie and
Lin, Hongyu and
Zheng, Jia and
Han, Xianpei and
Sun, Le",
editor = "Agrawal, Sweta and
Abad, Alberto",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-demos.4/",
pages = "36--45",
ISBN = "979-8-89176-461-3",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T ReasoningLens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models
%A Zhang, Jun
%A Zheng, Jiasheng
%A Cao, Boxi
%A Lu, Yaojie
%A Lin, Hongyu
%A Zheng, Jia
%A Han, Xianpei
%A Sun, Le
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F zhang-etal-2026-reasoninglens
%X 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.
%U https://aclanthology.org/2026.emnlp-demos.4/
%P 36-45
Markdown (Informal)
[ReasoningLens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models](https://aclanthology.org/2026.emnlp-demos.4/) (Zhang et al., EMNLP 2026)
ACL
- Jun Zhang, Jiasheng Zheng, Boxi Cao, Yaojie Lu, Hongyu Lin, Jia Zheng, Xianpei Han, and Le Sun. 2026. ReasoningLens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 36–45, Budapest, Hungary. Association for Computational Linguistics.