Reasoning models struggle to control their chains of thought, and that’s good
#reasoning models #chains of thought #AI control #cognitive processes #artificial intelligence #machine learning #creative outcomes
📌 Key Takeaways
- AI reasoning models often fail to manage their own thought processes effectively.
- This lack of control in reasoning chains is actually beneficial for certain applications.
- The article suggests that imperfect reasoning can lead to more creative or unexpected outcomes.
- Researchers are exploring how this limitation might be leveraged in AI development.
📖 Full Retelling
OpenAI introduces CoT-Control and finds reasoning models struggle to control their chains of thought, reinforcing monitorability as an AI safety safeguard.
🏷️ Themes
AI Reasoning, Cognitive Limitations
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Original Source
March 5, 2026 Research Safety Publication Reasoning models struggle to control their chains of thought, and that’s good Why a limitation of frontier models is reassuring for AI safety. Read the paper (opens in a new window) Loading… Share As AI agents become capable of carrying out increasingly complex and autonomous tasks, maintaining reliable oversight of their behavior becomes more important. Consistent with our principle of iterative deployment, we study how systems behave in real-world settings and continuously refine safeguards as capabilities advance. To support this, our safety approach uses defense-in-depth, with multiple complementary layers of defense such as safety training , behavioral testing , agentic code review (opens in a new window) , and chain-of-thought monitoring . CoT monitoring analyzes the reasoning steps agents generate while pursuing tasks. These reasoning traces can provide valuable signals during both training and deployment, helping monitoring systems identify when an agent’s behavior may be unsafe or inconsistent with the user’s intended goals. Today, we find that models’ reasoning is generally interpretable and easy to monitor . However, in the future, monitorability may break down for a variety of reasons (opens in a new window) . Here, we focus on one such path: if agents become capable of deliberately reshaping or obscuring their reasoning when they know they are being monitored, evaluations could overestimate a system’s alignment or safety, and monitoring systems could become less reliable. In this work, we study whether current reasoning models are capable of controlling their chain of thought in ways that reduce monitorability. Understanding this capability is important for ensuring that CoT monitoring remains a robust safeguard as AI systems grow more capable. We find that current reasoning models struggle to control their CoTs, even when told they are being monitored. While controllability is higher for larger mo...
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