Definition
For hard tasks a model must reason serially in its chain of thought, which acts as working memory. A monitor can read it and catch intent to misbehave before the model acts (Korbak et al., 2025: “a new and fragile opportunity”).
- Unfaithful: a hint that changes the answer is acknowledged in only 25% (Claude 3.7) or 39% (R1) of cases. Reward hacks are exploited >99% of the time but verbalized <2% (Chen et al., 2025).
- Fragile: outcome-based RL, pressure on the CoT, latent reasoning and learned obfuscation can all close the window.
- Don’t optimize against it: penalizing bad thoughts during training made the model keep reward hacking while no longer showing it in the CoT (Baker et al., 2025). This removes the transparency, not the behavior.