1 citations · 1 across the 1 of their papers we have counts for
9 papers
It's a TRAP! Task-Redirecting Agent Persuasion Benchmark for Web Agents
Karolina Korgul, Yushi Yang, Arkadiusz Drohomirecki +7
Web-based agents powered by large language models are increasingly used for tasks such as email management or professional networking. Their reliance on dynamic web content, howeve…
RepSelect: Robust LLM Unlearning via Representation Selectivity
Filip Sondej, Yushi Yang, Adam Mahdi
When LLM weights are open or fine-tuning is available through an API, suppressing hazardous knowledge and tendencies is not enough: removal has to be deep enough that an adversary…
Agentic Reinforcement Learning for Search Misaligns Instruction-Tuning
Yushi Yang, Shreyansh Padarha, Sarah Ball +2
Agentic reinforcement learning (RL) trains large language models to use tools, but its impact on alignment is poorly understood. We study how agentic RL for search affects the alig…
Agents' Last Exam
Yiyou Sun, Xinyang Han, Weichen Zhang +306
Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…
Robust LLM Unlearning with MUDMAN: Meta-Unlearning with Disruption Masking And Normalization
Filip Sondej, Yushi Yang, MikoÅaj Kniejski +1
Language models can retain dangerous knowledge and skills even after extensive safety fine-tuning, posing both misuse and misalignment risks. Recent studies show that even speciali…
Collapse of Irrelevant Representations (CIR) Ensures Robust and Non-Disruptive LLM Unlearning
Filip Sondej, Yushi Yang
Current unlearning and safety training methods consistently fail to remove dangerous knowledge from language models. We identify the root cause - unlearning targets representations…