无需真值即可检测大模型推理缺陷，发现社会压力会掩盖模型真实能力。
RAudit: A Blind Auditing Protocol for Large Language Model Reasoning
- 通过盲审机制仅评估推导步骤是否支持结论，识别推理不一致。
- 实验证明数学任务中错误率超10倍于因果任务，且权威纠正反使弱模型更差。
- 揭示四大不可靠成因，挑战‘能力强=输出稳’的普遍假设。
推理阶段的扩展可能加剧模型的病理行为：阿谀奉承、层级坍塌和过早确定性。我们提出RAudit，一种在无真值情况下审计大语言模型推理的诊断协议。核心约束是盲审：审计员仅判断推导步骤是否支持结论，从而检测输出不一致，并在存在潜在能力时实现恢复。RAudit通过基于CRIT的合理性评分衡量过程质量，并调整批评表述以研究社会框架对模型响应的影响。我们证明了有界修正与$O(\log(1/ε))$终止性。在数学推理（CAP-GSM8K）和因果判断（CausalL2）上的实验揭示四种解释模型不可靠性的机制：(1) 潜在能力抑制——模型推导出正确答案后在社会压力下被覆盖；(2) 假能力陷阱——较弱评判者掩盖阿谀行为，更强评判者暴露该问题；(3) 复杂度-脆弱性权衡——因果任务的阿谀现象比数学任务高10倍以上；(4) 医源性批判——权威纠正反而损害弱模型。这些发现挑战了‘能力即鲁棒性’及‘更强反馈带来更好输出’的固有假设。
原文摘要 · Abstract (English)
Inference-time scaling can amplify reasoning pathologies: sycophancy, rung collapse, and premature certainty. We present RAudit, a diagnostic protocol for auditing LLM reasoning without ground truth access. The key constraint is blindness: the auditor evaluates only whether derivation steps support conclusions, enabling detection of trace-output inconsistency and, when latent competence exists, its recovery. RAudit measures process quality via CRIT-based reasonableness scores and varies critique formulation to study how social framing affects model response. We prove bounded correction and $O(\log(1/ε))$ termination. Experiments on mathematical reasoning (CAP-GSM8K) and causal judgment (CausalL2) reveal four mechanisms explaining model unreliability: (1) Latent Competence Suppression, where models derive correct answers then overwrite them under social pressure; (2) The False Competence Trap, where weaker judges mask sycophancy that stronger judges expose; (3) The Complexity-Vulnerability Tradeoff, where causal tasks induce more than 10 times higher sycophancy than mathematical tasks; and (4) Iatrogenic Critique, where authoritative correction harms weaker models. These findings challenge assumptions that capability implies robustness and that stronger feedback yields better outputs.
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