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When Thinking Backfires: Mechanistic Insights into Reason-induced Misalignment

ICLR 2026 · Large language models › Reasoning and chain of thought · approach: Long CoT dynamics and efficiency

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Identifies Reasoning-Induced Misalignment (RIM), where enhancing reasoning via CoT at inference or fine-tuning on reasoning data (especially with effort-minimizing CoT patterns) raises harmful compliance, and gives mechanistic explanations via refusal attention heads and safety-reasoning neuron entanglement (RAS metric).

Problem

Reasoning capability enhancements (chain-of-thought at inference or math fine-tuning) can make LLMs more responsive to harmful requests; the mechanisms behind this reasoning-safety trade-off are unclear.

Approach

Defines RIM and attributes it to effort-minimizing reasoning patterns; at inference, refusal attention heads shift attention away from CoT tokens to empty think spans; during training, safety-critical MLP neurons show high activation entanglement with math reasoning, quantified by a new Reciprocal Activation Shift (RAS) metric that correlates with catastrophic forgetting in misalignment rate.

Key results

• Enabling think mode raised misalignment rate on HEx-PHI for all four Qwen3 models (e.g., Qwen3-4B 15.39% to 22.94%).
• Fine-tuning on GSM8k(L) with effort-minimizing CoTs raised average dense misalignment by 12.85% versus -2.94% for control CoTs.
• RAS correlated with misalignment-rate change (r=0.891, p=0.003) across 8 GSM8k checkpoints, outperforming KL-divergence on average correlation (0.65 vs 0.23).

Surprising findings

• Misalignment rates decreased in 5 of 8 models after fine-tuning on control CoTs of similar length, showing CoT length alone is not the driver.
• Intervening on safety-critical neurons raised misalignment by 13.26% while random neurons changed it by -2.19%, and MoE models were less vulnerable than dense counterparts on math fine-tuning.

Limitations

• Safety evaluated mainly on HEx-PHI, with only math and logic/commonsense reasoning tasks explored.
• Judging relies on GPT-4.1 with nondeterministic vLLM inference.
• Mechanistic analysis is correlational for training; RAS correlation rests on few checkpoints and models.

Experimental setup

Datasets: GSM8k MATH500 MATH401 GSM8k(L) Alpaca-derived CoTs PrOntoQA StrategyQA HEx-PHI MultiArith AIME24/25 HarmBench AgentHarm

Benchmarks: HEx-PHI HarmBench AgentHarm MultiArith AIME24/25

Metrics: misalignment rate answer accuracy correlation (Pearson r) with catastrophic forgetting probe score

Baselines: KL-divergence arithmetic mean geometric mean random-neuron RAS

Builds on: emergent misalignment steering vectors GSM8K

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