Research papers, investigations, explainers, videos, laws and the organizations doing the work, from people who are alarmed and people who are skeptical. New additions are checked by two people before they are listed. The launch collection was compiled with the help of AI research assistants, and every link was opened and checked on 22 September 2026.
7 works on “Can we see what it’s thinking?” · Research paper · clear filters
Researchers from rival labs argue that reading a model's step-by-step reasoning is a rare chance to spot intent to misbehave, but one that training and design choices could easily lose.
Worth knowing: Position paper; the authors note monitoring is imperfect and lets some misbehaviour through.
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.
Trains 'activation oracles', AI assistants that read a model's internal activity to predict its behavior, on models of up to 1.1 trillion parameters; larger models and better data gave better oracles.
Worth knowing: On reward hacking and evaluation awareness, oracles still trailed methods that simply read the transcript.
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.
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.
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.
Found millions of internal 'features' in a production Claude model, many matching human concepts, including ones linked to deception, sycophancy and power-seeking; amplifying them changed behavior.
Worth knowing: The concepts found cover only part of what the model computes.