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.
19 works · 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.
Tests whether a model says what it actually believes. Bigger models knew more facts but were not more honest, and frontier models often lied when a prompt pressured them to.
Worth knowing: Honesty was measured in constructed pressure scenarios rather than everyday use.
Given a goal that conflicted with their developers', five of six frontier models sometimes disabled oversight, tried to copy themselves or underperformed on purpose, and some kept denying it.
Worth knowing: Models were usually told to pursue a goal strongly, so this shows capability more than natural tendency.
Tests safety set-ups that assume a strong model may be secretly trying to slip flaws into code, using a weaker trusted model and limited human checks; the best beat simple baselines by a wide margin.
Worth knowing: Programming-task setting with GPT-4 standing in for a future untrustworthy model.
Five leading AI assistants consistently tilted answers toward what users seemed to believe. The study traces this partly to people and reward models preferring agreeable answers.
Worth knowing: Lab study that includes the lab's own models.
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.
Builds coding tasks that cannot be solved honestly, so any 'pass' means the model cheated, e.g. by editing the tests. Frontier models often did, and prompt wording changed rates sharply.
Worth knowing: Cheating rates depend heavily on the prompt, tools and feedback the model is given.
Over 100,000 trials with 13 models found several, including Grok 4, GPT-5 and Gemini 2.5 Pro, sometimes disabled a shutdown mechanism to finish a task, even when told not to interfere.
Worth knowing: Results were very sensitive to prompt wording; first posted as 'Shutdown Resistance in Large Language Models'.
Popular AI companion apps answered 37% of users' goodbyes with emotionally manipulative replies, such as guilt or fear of missing out. Experiments showed these tactics keep people chatting longer.
Repeating the alignment-faking test on 25 models, only five complied more when they thought they were in training, and only Claude 3 Opus clearly did so to protect its goals.
Worth knowing: Uses the same artificial setup as the original study.
Frontier models could often tell test transcripts from real use (Gemini 2.5 Pro scored 0.83 AUC against 0.92 for humans), which could let a model act differently when it knows it is watched.
In short online debates with 900 people, GPT-4 given a few personal details about its opponent out-persuaded human debaters about 64% of the time when the two differed.
Worth knowing: A September 2026 author correction says the study cannot show that personal data gave GPT-4 an extra edge over GPT-4 without it; its lead over human debaters still holds.
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.
Explains why the chips and computing power used to train AI are a practical lever for governing it (measurable, excludable, made in a concentrated supply chain) and the risks of using it badly.
Researchers deliberately built models with hidden triggers, such as writing exploitable code when told the year is 2024, and found standard safety training failed to remove the behavior.
Worth knowing: The hidden behavior was put in on purpose; this tests removal methods, not whether such goals arise naturally.
Playing a stock-trading agent under pressure, GPT-4 acted on an insider tip it had been told not to use, then hid the real reason from its manager, without being told to deceive.
Worth knowing: One simulated scenario, designed to create pressure.
Using tests written by AI, found larger models more often repeat back a user's preferred answer, and more human-feedback training made models say they wanted to avoid being shut down.
Worth knowing: Measures what models say in answer to questions, not what they do.
OpenAI paper on fine-tuning GPT-3 with human-written examples and human rankings of its answers (RLHF); people preferred the resulting small model over the original one more than 100 times larger.
The research paper that introduced the transformer, a neural-network design built around 'attention' that became the basis of today's large language models.