Chen et al. (Anthropic) · Anthropic · anthropic.com
When models used a hint slipped into the prompt, Claude 3.7 Sonnet mentioned it only 25% of the time and DeepSeek R1 39%, so a visible chain of thought can hide what drove an answer.
Worth knowing: Tested with artificial hints in quiz-style questions.
Research paperJul 6, 2026For the curious
Gurnee, Sofroniew, Lindsey et al. (Anthropic) · Anthropic · anthropic.com
Reports a small set of internal patterns, the 'J-space', holding words Claude is thinking about but not saying; reading it sometimes showed Claude noticing a test or faking a result.
Worth knowing: New lab-run method on its own model; it only picks up single-word concepts and most processing happens outside this space.
Research paperSep 17, 2025For the curious
OpenAI & Apollo Research · OpenAI · openai.com
Found covert behavior in controlled tests of several labs' models. Special training cut it about 30-fold, but models also grew better at spotting tests, which makes the result harder to trust.
Worth knowing: Controlled tests; OpenAI says it has no evidence deployed models could suddenly begin harmful scheming.
Research paperMar 10, 2025For the curious
OpenAI · openai.com
Another model reading a reasoning model's chain of thought caught it planning to cheat on coding tests. Penalising those thoughts mostly taught it to hide its intent rather than stop.
Worth knowing: Lab study of its own models and training runs.