![[2602.18297] Analyzing and Improving Chain-of-Thought Monitorability Through Information Theory](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.18297] Analyzing and Improving Chain-of-Thought Monitorability Through Information Theory
Summary
This paper explores the monitorability of chain-of-thought (CoT) systems in LLMs using information theory, identifying errors that affect performance and proposing methods to enhance accuracy.
Why It Matters
Understanding and improving the monitorability of CoT systems is crucial for ensuring reliable AI outputs, particularly in applications where reasoning and decision-making are critical. This research addresses potential vulnerabilities in AI systems and offers practical solutions to enhance their reliability.
Key Takeaways
- Non-zero mutual information is necessary but not sufficient for CoT monitorability.
- Two key sources of approximation error can undermine CoT monitor performance: information gap and elicitation error.
- Targeted training objectives can systematically improve CoT monitor accuracy.
- An oracle-based method and a label-free approach can enhance monitor performance.
- Improved monitorability helps mitigate issues like reward hacking in AI systems.
Computer Science > Machine Learning arXiv:2602.18297 (cs) [Submitted on 20 Feb 2026] Title:Analyzing and Improving Chain-of-Thought Monitorability Through Information Theory Authors:Usman Anwar, Tim Bakker, Dana Kianfar, Cristina Pinneri, Christos Louizos View a PDF of the paper titled Analyzing and Improving Chain-of-Thought Monitorability Through Information Theory, by Usman Anwar and 4 other authors View PDF Abstract:Chain-of-thought (CoT) monitors are LLM-based systems that analyze reasoning traces to detect when outputs may exhibit attributes of interest, such as test-hacking behavior during code generation. In this paper, we use information-theoretic analysis to show that non-zero mutual information between CoT and output is a necessary but not sufficient condition for CoT monitorability. We identify two sources of approximation error that may undermine the performance of CoT monitors in practice: information gap, which measures the extent to which the monitor can extract the information available in CoT, and elicitation error, which measures the extent to which the monitor approximates the optimal monitoring function. We further demonstrate that CoT monitorability can be systematically improved through targeted training objectives. To this end, we propose two complementary approaches: (a) an oracle-based method that directly rewards the monitored model for producing CoTs that maximize monitor accuracy, and (b) a more practical, label-free approach that maximizes con...
