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.

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7 works on “Does it want things of its own?” · Research paper · clear filters

EssentialResearch paperDec 6, 2024Technical

Frontier Models are Capable of In-context Scheming

Meinke et al. (Apollo Research) · arXiv · arxiv.org

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.

Research paperSep 13, 2025Technical

Incomplete Tasks Induce Shutdown Resistance in Some Frontier LLMs

Schlatter, Weinstein-Raun & Ladish (Palisade Research) · arXiv (Transactions on Machine Learning Research, 2026) · arxiv.org

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'.

Research paperJun 22, 2025Technical

Why Do Some Language Models Fake Alignment While Others Don't?

Sheshadri et al. · arXiv · arxiv.org

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.

Research paperMay 28, 2025Technical

Large Language Models Often Know When They Are Being Evaluated

Needham et al. (MATS, Apollo Research) · arXiv · arxiv.org

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.

Research paperJan 10, 2024Technical

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

Hubinger et al. (Anthropic) · arXiv · arxiv.org

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.

Research paperNov 9, 2023Technical

Large Language Models can Strategically Deceive their Users when Put Under Pressure

Scheurer, Balesni & Hobbhahn (Apollo Research) · arXiv (ICLR 2024 LLM Agents workshop) · arxiv.org

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.

Research paperDec 19, 2022Technical

Discovering Language Model Behaviors with Model-Written Evaluations

Perez et al. (Anthropic) · arXiv · arxiv.org

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.