3 papers
cs.CR2025
SIRAJ: Diverse and Efficient Red-Teaming for LLM Agents via Distilled Structured Reasoning
Kaiwen Zhou, Ahmed Elgohary, A S M Iftekhar +1
The ability of LLM agents to plan and invoke tools exposes them to new safety risks, making a comprehensive red-teaming system crucial for discovering vulnerabilities and ensuring…
cs.AI2025
When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration
Quan Shi, Carlos E. Jimenez, Shunyu Yao +3
Recent advancements in AI reasoning have driven substantial improvements across diverse tasks. A critical open question is whether these improvements also yields better knowledge t…
cs.AI2025
Contextual Experience Replay for Self-Improvement of Language Agents
Yitao Liu, Chenglei Si, Karthik Narasimhan +1
Large language model (LLM) agents have been applied to sequential decision-making tasks such as web navigation, but without any environment-specific experiences, they often fail in…