14 citations · 14 across the 2 of their papers we have counts for
6 papers
Measuring AI Ability to Complete Long Software Tasks
Thomas Kwa, Ben West, Joel Becker +23
Despite rapid progress on AI benchmarks, the real-world meaning of benchmark performance remains unclear. To quantify the capabilities of AI systems in terms of human capabilities,…
The 2026 Singapore Consensus on Global AI Safety Research Priorities
Stephen Casper, Oskar Galeev, Yoshua Bengio +117
Frontier AI capabilities and autonomy are advancing rapidly. A growing number of real-world incidents make a trusted AI ecosystem essential to embracing AI with confidence. The 202…
The Singapore Consensus on Global AI Safety Research Priorities
Yoshua Bengio, Tegan Maharaj, Luke Ong +84
Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to ensure that AI is safe, i.e., trustworthy,…
RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts
Hjalmar Wijk, Tao Lin, Joel Becker +20
Frontier AI safety policies highlight automation of AI research and development (R&D) by AI agents as an important capability to anticipate. However, there exist few evaluations fo…
HCAST: Human-Calibrated Autonomy Software Tasks
David Rein, Joel Becker, Amy Deng +19
To understand and predict the societal impacts of highly autonomous AI systems, we need benchmarks with grounding, i.e., metrics that directly connect AI performance to real-world…
DarkBench: Benchmarking Dark Patterns in Large Language Models
Esben Kran, Hieu Minh "Jord" Nguyen, Akash Kundu +3
We introduce DarkBench, a comprehensive benchmark for detecting dark design patterns--manipulative techniques that influence user behavior--in interactions with large language mode…