164 citations · 185 across the 7 of their papers we have counts for
4 papers · 1 filter
EVMbench: Evaluating AI Agents on Smart Contract Security
Justin Wang, Andreas Bigger, Xiaohai Xu +5
Smart contracts on public blockchains now manage large amounts of value, and vulnerabilities in these systems can lead to substantial losses. As AI agents become more capable at re…
GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks
Tejal Patwardhan, Rachel Dias, Elizabeth Proehl +16
We introduce GDPval, a benchmark evaluating AI model capabilities on real-world economically valuable tasks. GDPval covers the majority of U.S. Bureau of Labor Statistics Work Acti…
Estimating Worst-Case Frontier Risks of Open-Weight LLMs
Eric Wallace, Olivia Watkins, Miles Wang +2
In this paper, we study the worst-case frontier risks of releasing gpt-oss. We introduce malicious fine-tuning (MFT), where we attempt to elicit maximum capabilities by fine-tuning…
Persona Features Control Emergent Misalignment
Miles Wang, Tom Dupré la Tour, Olivia Watkins +8
Understanding how language models generalize behaviors from their training to a broader deployment distribution is an important problem in AI safety. Betley et al. discovered that…