A False Average: Pooled CoT-Monitor Accuracy Conceals a Reasoning-Dependent Fragility
On the map: LLM reasoning → Prompting and chain-of-thought reasoning
Abstract (arXiv)
Chain-of-thought (CoT) monitors are reported by their aggregate accuracy on a pool of reward hacks. We show that this number is a false average. On Terminal Wrench, about 77% of hacks are given away by the actions alone, and the monitor's pooled accuracy is dominated by them; on the remaining 23%, where the reasoning is the only signal, the same monitor is fragile. We expose the fragility with a controlled attack: we rewrite only the agent's reasoning to read as good-faith engineering, leaving every command and output byte-identical, so the exploit is unchanged. One gradient-free rewrite drops a held-out monitor's catch rate on that subset from about 95% to between 4 and 11%, while the pooled rate falls only about 25 points, the subset's share of the pool times its collapse. The rewrite stays truthful about what happened and lies only about intent, so a monitor that reads only the trace recovers little, even when warned about the attack, and only information from outside the trace helps much. The attack transfers across monitor and agent families, and the dataset's own hack labels show the same gradient with no monitor in the definition. Live agents on 27 tasks produce hollow hacks unaided, but a monitor calibrated on each model's own honest solves catches nearly all of them (AUC at least 0.996 under the narration instruction, 0.97 without it); a small residual has the blindspot's shape, low scores on both actions and narration, though none falls below the calibrated threshold. A linear probe on a surrogate monitor's activations out-reads its verdict on the obfuscated hacks (AUC up to 0.98), but a behavioral control shows this edge does not, by itself, mean the monitor knows more than it says. Monitor accuracy should be reported on the reasoning-dependent subset, where the defense is supportive.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 22 of its 32 references that are in the map. First posted on arXiv 1 Aug 2026.
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22 of 32 references.
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- 2026The Hypocrisy Gap: Quantifying Divergence Between Internal Belief and Chain-of-Thought Explanation via Sparse Autoencoders14 Jan
- 2025Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety15 Jul
- 2025When Chain of Thought is Necessary, Language Models Struggle to Evade Monitors7 Jul
- 2025CoT Red-Handed: Stress Testing Chain-of-Thought Monitoring29 May
- 2025Reasoning Models Don't Always Say What They Think8 May
- 2025Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation14 Mar
- 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting7 May
Other references · 15
- 2024Refusal in Language Models Is Mediated by a Single Directioncited by 960
- 2023AI Control: Improving Safety Despite Intentional Subversioncited by 207
- 2024Optimization-based Prompt Injection Attack to LLM-as-a-Judgecited by 123
- 2025Detecting Strategic Deception Using Linear Probescited by 65
- 2024Obfuscated Activations Bypass LLM Latent-Space Defensescited by 40
- 2026Building Production-Ready Probes For Geminicited by 38
- 2025RL-Obfuscation: Can Language Models Learn to Evade Latent-Space Monitors?cited by 19
- 2025Benchmarking Deception Probes via Black-to-White Performance Boostscited by 17
- 2026Terminal Wrench: A Dataset of 331 Reward-Hackable Environments and 3,632 Exploit Trajectoriescited by 14
- 2026How does information access affect LLM monitors' ability to detect sabotage?cited by 12
- 2026The Obfuscation Atlas: Mapping Where Honesty Emerges in RLVR with Deception Probescited by 6
- 2026ProMoral-Bench: Evaluating Prompting Strategies for Moral Reasoning and Safety in LLMscited by 4
3 more
- 2025But what is your honest answer? Aiding LLM-judges with honest alternatives using steering vectorscited by 3
- 2026Where Reliability Lives in Vision-Language Models: A Mechanistic Study of Attention, Hidden States, and Causal Circuitscited by 3
- 2025COMPASS: Context-Modulated PID Attention Steering System for Hallucination Mitigationcited by 2
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- 2026Corrupt Plans, Clean Traces: Evading Chain-of-Thought Monitoring with Plan Injection
- 2026Evading Chain-of-Thought Monitoring Through Model Poisoning
- 2026Monitor Jailbreaking: Evading Chain-of-Thought Monitoring Without Encoded Reasoning
- 2026Chain-of-thought obfuscation learned from output supervision can generalise to unseen tasks
- 2026SLEIGHT-Bench: A Benchmark of Evasion Attacks Against Agent Monitors