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?” · clear filters
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.
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.
Researchers look inside the Claude model and find it plans rhyming words ahead, shares concepts across languages, and can offer plausible reasoning that is not how it actually reached an answer.
Worth knowing: Research by the model's own developer; the authors say their tools capture only a fraction of the model's computation.
A tool that uses AI to summarize, search and cluster long AI-agent transcripts, helping researchers spot broken tasks, unexpected behavior and weaknesses that a single score hides.
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.
Nonprofit lab building open tools to understand and oversee AI systems, including its Docent analysis tool and public reports on how models behave, such as its mental-health evaluation.