Chain-of-Thought Reasoning in the Wild Is Not Always Faithful
This academic paper investigates the reliability of chain-of-thought reasoning in large language models. The authors argue that these reasoning processes are not always faithful to the model's actual decision-making path.
Why it matters
As AI models are increasingly used for complex tasks, understanding whether their reasoning is transparent or merely performative is critical for safety and reliability.
Focus to learn more arXiv-issued DOI via DataCite Submission history From: Iván Arcuschin [ view email ] [v1] Tue, 11 Mar 2025 17:56:30 UTC (4,311 KB) [v2] Thu, 13 Mar 2025 17:49:58 UTC (4,348 KB) [v3] Wed, 19 Mar 2025 19:20:42 UTC (4,349 KB) [v4] Tue, 17 Jun 2025 17:59:57 UTC (2,337 KB) [v5] Fri, 29 May 2026 17:38:22 UTC (2,378 KB) [v6] Tue, 16 Jun 2026 17:36:22 UTC (2,378 KB) Full-text links: Access Paper: View a PDF of the paper titled Chain-of-Thought Reasoning In The Wild Is Not Always Faithful, by Iv\'an Arcuschin and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2025-03 Change to browse by: cs cs.CL cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation loading...
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