Luca Righetti shares takeaways on the role of randomized controlled trials in AI safety testing.

Amy Deng investigates whether coding agent transcripts could serve as an alternative for estimating AI productivity uplift, using 5305 Claude Code transcripts from METR technical staff.

Nikola Jurkovic describes our measurements of time horizon using Claude Code and Codex scaffolds.

Thomas Kwa describes a simple model for forecasting when AI will automate AI development, based on the AI Futures model but with only 8 parameters.

Miles Kodama and Michael Chen summarize key provisions from California's SB 53, the EU Code of Practice, and New York's RAISE Act covering frontier AI developers.

Thomas Kwa responds to some misinterpretations of our time horizon work, and explains limitations and the core finding.

Vincent Cheng, Thomas Kwa, and Neev Parikh share research on how AI agents can hide secondary task-solving from monitors, finding that harder tasks are more detectable and small models can learn to evade larger monitors.

Vincent Cheng and Thomas Kwa replicate a Google DeepMind paper on chain-of-thought monitoring, showing evidence that monitoring works on other companies' models.