Korbak, T., et al. (2025).
arXiv:2507.11473
Abstract
AI systems that “think” in human language offer a unique opportunity for AI safety: we can monitor their chains of thought (CoT) for the intent to misbehave. Like all other known AI oversight methods, CoT monitoring is imperfect and allows some misbehavior to go unnoticed. Nevertheless, it shows promise and we recommend further research into CoT monitorability and investment in CoT monitoring alongside existing safety methods. Because CoT monitorability may be fragile, we recommend that frontier model developers consider the impact of development decisions on CoT monitorability.
Here are some thoughts:
The paper highlights a unique moment in AI development, where large language models reason in human language, making their decisions interpretable through visible “chain of thought” (CoT) processes. This human-readable reasoning enables researchers to audit, monitor, and potentially catch misaligned or risky behaviors by reviewing the model's intermediary steps rather than just its final outputs.
While CoT monitoring presents new possibilities for AI oversight and transparency, the paper emphasizes its fragility: monitorability can decrease if model training shifts toward less interpretable methods or if models become incentivized to obscure their thoughts. The authors caution that CoT traces may not always faithfully represent internal reasoning and that models might find ways to hide misbehavior regardless. They call for further research into how much trust can be placed in CoT monitoring, the development of benchmarks for faithfulness and transparency, and architectural choices that preserve monitorability.
Ultimately, the paper urges AI developers to treat CoT monitorability as a valuable but unstable safety layer, advocating for its inclusion alongside—but not in place of—other oversight and alignment strategies
