5 papers · 1 filter
Evaluating Model Capabilities After Benchmark Saturation
Morio Hamada, Jehyeok Yeon, Matteo Migliarini +2
Rapid advances in AI capabilities are causing benchmarks to saturate. Once several models approach perfect scores, a benchmark loses its power to differentiate between them, and wi…
ResearchArena: Evaluating Sabotage and Monitoring in Automated AI R&D
Lena Libon, Ben Rank, Jehyeok Yeon +5
As AI agents begin to automate AI R&D, we need ways to assess whether their outputs are safe to deploy, even when the agents themselves may be untrusted. AI control offers one such…
Securing Multimodal AI through Internal Information Decomposition
Jehyeok Yeon, Hyeonjeong Ha, Qiusi Zhan +1
Multimodal large language models introduce attack surfaces absent in unimodal systems: adversaries can distribute malicious intent across modalities to evade unimodal safeguards. T…
InferenceBench: A Benchmark for Open-Ended LLM Inference Optimization by AI Agents
Jehyeok Yeon, Ben Rank, Maksym Andriushchenko
AI agents are increasingly used to automate research and development tasks, yet existing benchmarks typically evaluate them on prescribed workflows or narrow action spaces. Even no…
TRAP: Targeted Redirecting of Agentic Preferences
Hangoo Kang, Jehyeok Yeon, Gagandeep Singh
Autonomous agentic AI systems powered by vision-language models (VLMs) are rapidly advancing toward real-world deployment, yet their cross-modal reasoning capabilities introduce ne…