Computer Science > Computation and Language
arXiv:2509.00544 (cs)
[Submitted on 30 Aug 2025 (v1), last revised 9 Mar 2026 (this version, v4)]
Title:When Thinking Backfires: Mechanistic Insights Into Reasoning-Induced Misalignment
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Abstract:With the growing accessibility and wide adoption of large language models, concerns about their safety and alignment with human values have become paramount. In this paper, we identify a concerning phenomenon: Reasoning-Induced Misalignment (RIM), in which misalignment emerges when reasoning capabilities strengthened-particularly when specific types of reasoning patterns are introduced during inference or training. Beyond reporting this vulnerability, we provide the first mechanistic account of its origins. Through representation analysis, we discover that specific attention heads facilitate refusal by reducing their attention to CoT tokens, a mechanism that modulates the model's rationalization process during inference. During training, we find significantly higher activation entanglement between reasoning and safety in safety-critical neurons than in control neurons, particularly after fine-tuning with those identified reasoning patterns. This entanglement strongly correlates with catastrophic forgetting, providing a neuron-level explanation for RIM.
| Comments: | |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2509.00544 [cs.CL] |
| (or arXiv:2509.00544v4 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2509.00544
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Submission history
From: Hainiu Xu [view email][v1] Sat, 30 Aug 2025 16:04:54 UTC (683 KB)
[v2] Sun, 28 Sep 2025 22:36:22 UTC (739 KB)
[v3] Mon, 13 Oct 2025 10:53:43 UTC (739 KB)
[v4] Mon, 9 Mar 2026 21:29:30 UTC (838 KB)
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View a PDF of the paper titled When Thinking Backfires: Mechanistic Insights Into Reasoning-Induced Misalignment, by Hanqi Yan and 4 other authors
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