Investigating CoT Monitorability in Large Reasoning Models
Shu Yang, Junchao Wu, Xilin Gong, Xuansheng Wu, Derek Wong, Ninghao Liu, Di Wang
TL;DR
This paper systematically investigates the potential and challenges of using chain-of-thought reasoning traces in large models to monitor and detect misbehavior, highlighting issues of faithfulness and reliability.
Contribution
It introduces the first comprehensive analysis of CoT monitorability, examining verbalization fidelity, monitor reliability, and the impact of intervention methods, and proposes the MoME paradigm for structured model monitoring.
Findings
Verbalization quality correlates with monitoring reliability.
Intervention methods can improve or impair monitor effectiveness.
Empirical evidence across multiple domains supports the analysis.
Abstract
Large Reasoning Models (LRMs) have demonstrated remarkable performance on complex tasks by engaging in extended reasoning before producing final answers. Beyond improving abilities, these detailed reasoning traces also create a new opportunity for AI safety, CoT Monitorability: monitoring potential model misbehavior, such as the use of shortcuts or sycophancy, through their chain-of-thought (CoT) during decision-making. However, two key fundamental challenges arise when attempting to build more effective monitors through CoT analysis. First, as prior research on CoT faithfulness has pointed out, models do not always truthfully represent their internal decision-making in the generated reasoning. Second, monitors themselves may be either overly sensitive or insufficiently sensitive, and can potentially be deceived by models' long, elaborate reasoning traces. In this paper, we present the…
Peer Reviews
Decision·ICLR 2026 Conference Withdrawn Submission
The paper aims to evaluate the faithfulness of the CoT and how it can detect potential misbehavior in an LLM. The topic is timely and offers a new perspective for understanding and supervising LLMs.
1. The main limitation lies in the lack of formal and rigorous evaluation objectives. The paper does not clearly define or quantify concepts such as unfaithful explanations or monitor reliability within CoT. For example, does CoT fidelity depend solely on whether the model explicitly references the prompt? Should it be considered faithful if the semantic content aligns despite differing expressions? Or if a model appears to mention the prompt but merely parroted it as an explanation, should this
The paper makes a meaningful contribution to AI safety by systematically analyzing CoT monitorability. * Clear conceptual framing: Separates CoT faithfulness and monitor reliability, which are often conflated. * Novel, interpretable metrics: Robustness and Scheming for model behavior; EEMR and OSM for monitor performance. * Comprehensive evaluation: Six LRMs, multiple datasets (AIME, GPQA, MMLU moral), and eight adversarial cue types. Empirical insights worth highlighting: * CIR barely decrea
While the paper is strong conceptually, several issues limit clarity and generalization: * Intro clarity: Metrics appear abruptly; better to explain intuition first and formalize later. * Scope limitation: All tasks are single-turn MCQs; no multi-turn or tool-use settings. * Model diversity: Mostly Qwen-derived; no LLaMA/GPT baselines. * Metric assumptions: Multiplicative definitions assume independence; item-level conditional analysis missing. * Monitor generalization: MoME trained on Qwen tra
- A systematic study of CoT monitorability on two axes important for the problem: faithfulness and monitor reliability. - The benchmarks cover multiple domains and models. - The insights on CoT compression hampering CoT monitoring and that longer reasoning traces make scheming more difficult are potentially useful for the community
- Benchmarks focus only on multiple-choice tasks but open-ended scenarios are more common in practice - The MoME framework evaluation is not convincing; the authors use a different metric (Monitor Effectiveness Score) in that section compared to the ones in Table 2, making comparison with existing methods difficult. Furthermore, MoME is finetuned for the task at hand, whereas the other monitors were only prompt adjusted. For a more fair comparison, I would have expected that the CoT monitors wer
- It is good that a number of existing, multi-domain datasets are used in this work. The number of models investigated is also a strength; one notes that there are only two families of model but also that there are not many open source LRMs that scale. - The precision/formalization of the metrics is noted, and the _general_ reproducibility of the work appears strong, at least regarding most of the empircal setup, though with caveats (below). - The evidence of oversensitivity is pursuasive; the l
- It is hard to determine the extent to which the current work is truly novel. Some existing work is cited, but it is essentially discarded as "intuitive speculation", although that may be premature. Turpin et al (2025) includes concrete experiemnts (as does Turpim et al (2023)) and Baker et al (2025) show that LLMs-as-monitor can detect reward-hacking by reading another model’s CoT, can compare monitoring strategies, etc. - Despite the positive nature of the multi-domain datasets noted above, i
- Experiments around prompt verbalization across different models. Self-explanation increases robustness, while CoT compression has detrimental effects. Cannot hurt to have a paper showing this. - Apparently, GPT-5-nano and Gemini-2.5-flash-lite are both not great at monitoring CoTs. A model trained to generate structured judgments (MoME) improves on this. - All of this seems sound and believable, so no huge issues around this.
- This work doesn't sound too surprising. We knew that current LRM models are not entirely faithful in their CoTs, and that there would be mismatches. - Also doesn't come as a surprise that GPT-5-nano or Gemini-2.5-flash do not perform great at monitoring CoTs. - The proposed method MoME fixing this, I'm sure it works, but it doesn't seem very inspirational. E.g., Why does it have to be this, why can't a model trained on appropriate data without structured judgement do this? - So, taken togethe
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Ethics and Social Impacts of AI · Decision-Making and Behavioral Economics