RAudit: A Blind Auditing Protocol for Large Language Model Reasoning

• URL: http://arxiv.org/abs/2601.23133v1
• Date: Fri, 30 Jan 2026 16:22:45 GMT
• Title: RAudit: A Blind Auditing Protocol for Large Language Model Reasoning
• Authors: Edward Y. Chang, Longling Geng,
• Abstract summary: Inference-time scaling can amplify reasoning pathologies: sycophancy, rung collapse, and premature certainty.<n>We present RAudit, a diagnostic protocol for auditing LLM reasoning without ground truth access.
• Score: 0.8594140167290097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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