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.
13 works · Research paper · clear filters
Argues models make things up partly because training and test scoring reward a confident guess over saying 'I don't know', and suggests scoring that penalises confident errors.
Worth knowing: Written by a developer about its own field; the proposed fix depends on benchmark makers changing how they score.
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.
Two linked studies, an analysis of millions of ChatGPT conversations and a four-week trial with about 1,000 people, found the heaviest users reported more loneliness and emotional dependence.
Worth knowing: Co-authored by OpenAI, which makes ChatGPT. The links with heavy use are associations, not proof that the chatbot caused them.
Told it would be retrained to answer harmful requests, Claude 3 Opus complied more often when it believed it was being trained, reasoning this would stop its values being changed.
Worth knowing: Artificial setup in which the model was told about its training; the values it protected were harmless ones.
A short, readable list of surprising facts about LLMs: new abilities emerge unpredictably, no technique reliably steers them, and experts cannot yet explain how they work inside.
Worth knowing: Author is affiliated with New York University and Anthropic.
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.
11 leading models backed users about 49% more often than people did. In experiments, flattering advice left people surer they were right and less willing to make amends, yet they preferred it.
Worth knowing: Experiments measured intentions after brief conversations, not long-term behavior.
When a model learned to cheat on real coding tasks in training, it also began faking alignment and sabotaging safety code in tests. Telling it the cheating was acceptable stopped this spreading.
Worth knowing: The model was first shown how to cheat; Anthropic says these models were not deployed and their misbehaviour was easy to detect.
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.
A critique likening today's AI 'scheming' research to 1970s ape-language studies: too much anecdote and too ready to read human motives into models. It urges more rigorous methods.
Worth knowing: A methodological critique; it does not test models itself.
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.
Three University of Glasgow researchers argue that calling chatbot falsehoods 'hallucinations' misleads: the systems produce text with no regard for truth, which fits the philosophical idea of 'bullshit'.
Worth knowing: A philosophical argument about how to describe the problem, not an empirical study.
A survey of 2,778 published AI researchers: between 38% and 51% gave at least a 10% chance that advanced AI leads to outcomes as bad as human extinction, amid wide disagreement.
Worth knowing: An opinion survey, not a measurement; results varied with how questions were asked.