Introducing v0.5 of the AI Safety Benchmark from MLCommons. - Bertie Vidgen; Adarsh Agrawal; Ahmed M. Ahmed 004; Victor Akinwande; Namir Al-Nuaimi; Najla Alfaraj; Elie Alhajjar; Lora Aroyo; Trupti Bavalatti; Borhane Blili-Hamelin; Kurt D. Bollacker; Rishi Bomassani; et al. (CoRR, 2024)

Affirmative safety: An approach to risk management for high-risk AI. - Akash R. Wasil; Joshua Clymer; David Krueger 001; Emily Dardaman; Siméon Campos; Evan R. Murphy (CoRR, 2024)

A Frontier AI Risk Management Framework: Bridging the Gap Between Current AI Practices and Established Risk Management. - Siméon Campos; Henry Papadatos; Fabien Roger; Chloé Touzet; Otter Quarks; Malcolm Murray (CoRR, 2025)

Mapping AI Benchmark Data to Quantitative Risk Estimates Through Expert Elicitation. - Malcolm Murray; Henry Papadatos; Otter Quarks; Pierre-François Gimenez; Siméon Campos (CoRR, 2025)

Evaluating the Goal-Directedness of Large Language Models. - Tom Everitt; Cristina Garbacea; Alexis Bellot; Jonathan Richens; Henry Papadatos; Siméon Campos; Rohin Shah (CoRR, 2025)

Existing Industry Practice for the EU AI Act's General-Purpose AI Code of Practice Safety and Security Measures. - Lily Stelling; Mick Yang; Rokas Gipiskis; Leon Staufer; Ze Shen Chin; Siméon Campos; Michael Chen (CoRR, 2025)

The Singapore Consensus on Global AI Safety Research Priorities. - Yoshua Bengio; Tegan Maharaj; Luke Ong; Stuart Russell 001; Dawn Song; Max Tegmark; Lan Xue; Ya-Qin Zhang; Stephen Casper; Wan Sie Lee; Sören Mindermann; Vanessa Wilfred; et al. (CoRR, 2025)