Committed Before Reasoning: Behavioral Reproduction and Preliminary Activation-Level Evidence of Answer Pre-Commitment in an Open-Weight LLM
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Abstract (arXiv)
Chat models sometimes commit to an answer and then produce reasoning that justifies it rather than deriving it -- even when the answer contradicts a task premise. We study a minimal probe: "I want to wash my car. The car wash is 100 meters away. Should I walk or drive?" Only drive works (the car must be at the car wash), yet models overwhelmingly recommend walking. (1) Behavioral reproduction: on Qwen3-8B across five system-prompt conditions (210 rollouts), the wrong commitment occurs in 85-100% of sampled rollouts per condition and 100% of greedy rollouts, in both thinking and non-thinking modes; a 4,096-token thinking budget does not repair it. (2) Preliminary activation-level evidence: probing hidden states with a pretrained, training-free activation oracle (no task-specific probe training) at positions before the answer text is emitted, "walk" read-outs exceed a neutral-context baseline (68% vs. 17%; walk-committing rollouts p=.005, drive-committing rollouts p=.005, Fisher exact) -- notably, rollouts that eventually answer drive also read as walk-leaning before commitment (5/6). The oracle's default on unrelated content is "drive" (83%), so the read-outs are not lexical bias; stratifying by literal walk/drive occurrence shows they are not text recovery either (spans containing "drive" still read out walk; in balanced lexical fields, per-rollout walk-majorities beat a per-prompt neutral baseline 15/22 vs. 1/8, p=.01; drive-committing rollouts 6/6, p=.002). Samples are small and the within-rollout positional gradient is not significant (p=.34); we frame these results as preliminary. (3) Methodological: with fixed oracle, activations, and positions, question wording alone moves a positive control from 2/16 (open question) to 11/16 (closed); negative oracle results are uninterpretable without per-wording positive controls.
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References · 10
- 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,081
- 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 668
- 2025Reasoning Models Don't Always Say What They Thinkcited by 388
- 2024LatentQA: Teaching LLMs to Decode Activations Into Natural Languagecited by 49
- 2025Activation Oracles: Training and Evaluating LLMs as General-Purpose Activation Explainerscited by 41
- 2026Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thoughtcited by 35
- 2026Post-Hoc Reasoning in Chain of Thought: Decoding and Steering Pre-Committed Answerscited by 6
- 2026Catching rationalization in the act: detecting motivated reasoning before and after CoT via activation probingcited by 6
- 2026Beyond the Commitment Boundary: Probing Epiphenomenal Chain-of-Thought in Large Reasoning Modelscited by 4
- 2026Therefore I am. I Thinkcited by 2
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- 2026Understanding and Mitigating Premature Confidence for Better LLM Reasoning
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- 2026Chain-of-Thought Faithfulness of Reasoning Models Varies with Where and How Preference Cues Are Delivered
- 2026Remember, Verify, or Ask? Cross-Family Evaluation of Memory Commitment in LLM Agents
- 2026The Detection-Extraction Gap: Models Know the Answer Before They Can Say It