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cs.CL2026
Do Proactive Agents Need an LLM to Decide When to Act?
Xiaoze Liu, Ruowang Zhang, Amir H. Abdi +5
Proactive assistants continuously decide when to intervene and what context should support the intervention. Large language model (LLM) pipelines repeatedly interpret activity hist…
cs.CL2025
Llama See, Llama Do: A Mechanistic Perspective on Contextual Entrainment and Distraction in LLMs
Jingcheng Niu, Xingdi Yuan, Tong Wang +2
We observe a novel phenomenon, contextual entrainment, across a wide range of language models (LMs) and prompt settings, providing a new mechanistic perspective on how LMs become d…
cs.CL2025
DefenderBench: A Toolkit for Evaluating Language Agents in Cybersecurity Environments
Chiyu Zhang, Marc-Alexandre Cote, Michael Albada +6
Large language model (LLM) agents have shown impressive capabilities in human language comprehension and reasoning, yet their potential in cybersecurity remains underexplored. We i…