Library

Everything worth reading about AI and human control. In one place.

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.

3 works on “Does it cheat to win?” · Research paper · clear filters

Research paperNov 21, 2025For the curious

From shortcuts to sabotage: natural emergent misalignment from reward hacking

Anthropic · anthropic.com

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.

Research paperOct 23, 2025Technical

ImpossibleBench: Measuring LLMs' Propensity of Exploiting Test Cases

Zhong, Raghunathan & Carlini · arXiv · arxiv.org

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.

Research paperMar 10, 2025For the curious

Detecting misbehavior in frontier reasoning models

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.