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When Confidence Rises Too Early: Detecting Shortcut Reasoning via Premature Answer Commitment

NeurIPS 2026 · Large language models › Reasoning and chain of thought · approach: Search and self-correction in reasoning · 0 citations (OpenAlex)

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Proposes ConfLens, which tracks LLM answer confidence across reasoning steps, and DACS, an entropy-based distributional confidence score, to detect shortcut reasoning and steer reward models toward faithful responses.

Problem

Detecting shortcut reasoning, where LLMs reach answers via hints or reward loopholes while post-rationalizing with plausible CoT, is hard because existing monitors inspect text or outcomes and existing confidence estimators need ground truth, are unreliable, or are too slow.

Approach

Track confidence in the final answer across CoT prefixes (ConfLens) and estimate confidence as negative entropy of the next-token distribution after a forced answer tag (DACS); shortcut samples show premature confidence early in reasoning. Detection signals are appended to reward model inputs as warnings.

Key results

• On math reasoning with Qwen2.5-3B-Instruct, DACS-based ConfLens reaches 0.784 AUROC in the explicit hint setting versus 0.702 for TRACE's AUROC baseline... (ConfLens F1 0.732 vs TRACE 0.658 in explicit hint).
• ConfLens is roughly 15x faster than TRACE (about 0.35 s vs 3.82 s per query on math with Qwen2.5-3B).
• Reward models accept shortcut-true responses; the accuracy-faithfulness gap is 9.4% on math and 7.15% on code for current reward models.

Surprising findings

• Models under shortcut reasoning tend to report lower self-reported confidence (sandbagging), so self-reported confidence methods are unreliable.
• Sequence likelihood fails to detect shortcuts under reward bias (AUROC 0.100 on math), even though it works well under hint settings.

Limitations

• Limited to verifiable tasks (math and code); not extended to subjective or open-ended tasks.
• Does not address broader in-the-wild reward hacking such as length bias or proxy over-optimization.
• DACS uses raw entropy without calibration.

Experimental setup

Datasets: Big-Math-Verified CRUXEval RewardBench2

Benchmarks: Big-Math-Verified CRUXEval

Metrics: AUROC F1 latency (s) accuracy of accepted responses faithfulness of accepted responses

Baselines: CoT Monitor TRACE Sequence Likelihood Self-consistency P(pass) Verbalized confidence

Builds on: GRPO CoT monitoring TRACE RewardBench 2

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