Long-form reasoning (CoT) is a double-edged sword. While reasoning models are smarter than ever, debugging a 10,000-token reasoning trace is a nightmare. ReasoningLens turns that \"Wall of Text\" into an interactive, hierarchical map.</p>\n","updatedAt":"2026-06-30T02:45:31.199Z","author":{"_id":"5fc052241160c47d1d438556","avatarUrl":"/avatars/f508707fc92f1b42f7897b12b727754c.svg","fullname":"Boxi Cao","name":"Bowieee","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":5,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.9016878008842468},"editors":["Bowieee"],"editorAvatarUrls":["/avatars/f508707fc92f1b42f7897b12b727754c.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2606.23404","authors":[{"_id":"6a432d08763f63ca3757e85e","name":"Jun Zhang","hidden":false},{"_id":"6a432d08763f63ca3757e85f","name":"Jiasheng Zheng","hidden":false},{"_id":"6a432d08763f63ca3757e860","name":"Boxi Cao","hidden":false},{"_id":"6a432d08763f63ca3757e861","name":"Yaojie Lu","hidden":false},{"_id":"6a432d08763f63ca3757e862","name":"Hongyu Lin","hidden":false},{"_id":"6a432d08763f63ca3757e863","name":"Jia Zheng","hidden":false},{"_id":"6a432d08763f63ca3757e864","name":"Xianpei Han","hidden":false},{"_id":"6a432d08763f63ca3757e865","name":"Le Sun","hidden":false}],"publishedAt":"2026-06-22T00:00:00.000Z","submittedOnDailyAt":"2026-06-30T00:00:00.000Z","title":"ReasoningLens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models","submittedOnDailyBy":{"_id":"5fc052241160c47d1d438556","avatarUrl":"/avatars/f508707fc92f1b42f7897b12b727754c.svg","isPro":false,"fullname":"Boxi Cao","user":"Bowieee","type":"user","name":"Bowieee"},"summary":"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.","upvotes":3,"discussionId":"6a432d08763f63ca3757e866","githubRepo":"https://github.com/icip-cas/ReasoningLens","githubRepoAddedBy":"user","ai_summary":"ReasoningLens is an open-source framework that provides hierarchical visualization and diagnostic auditing for complex reasoning chains in large reasoning models, enabling structured analysis and error detection through interactive hierarchies and automated auditing.","ai_keywords":["Chain-of-Thought traces","hierarchical visualization","diagnostic auditing","information necropsy","agentic auditor","tool-augmented verification","systemic reasoning profiles"],"ai_summary_model":"Qwen/Qwen2.5-Coder-32B-Instruct","githubStars":24},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"5fc052241160c47d1d438556","avatarUrl":"/avatars/f508707fc92f1b42f7897b12b727754c.svg","isPro":false,"fullname":"Boxi Cao","user":"Bowieee","type":"user"},{"_id":"6a2da6c8ca070ee12c6e396c","avatarUrl":"/avatars/0355287dcabaa67dbc7f0b10b87451f9.svg","isPro":false,"fullname":"Joe Mama","user":"JoeMama123123123","type":"user"},{"_id":"63ac5701c21e60a3e9b58aa7","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63ac5701c21e60a3e9b58aa7/g6EX7diOpuA94R2ab-rZC.png","isPro":true,"fullname":"Dipankar Sarkar","user":"dipankarsarkar","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"query":{}}">
ReasoningLens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models
Abstract
ReasoningLens is an open-source framework that provides hierarchical visualization and diagnostic auditing for complex reasoning chains in large reasoning models, enabling structured analysis and error detection through interactive hierarchies and automated auditing.
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.
Community
Long-form reasoning (CoT) is a double-edged sword. While reasoning models are smarter than ever, debugging a 10,000-token reasoning trace is a nightmare. ReasoningLens turns that "Wall of Text" into an interactive, hierarchical map.
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Cite arxiv.org/abs/2606.23404 in a model README.md to link it from this page.
Cite arxiv.org/abs/2606.23404 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2606.23404 in a Space README.md to link it from this page.
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