most citedThe PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies

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cs.AI2026

GoodPoint: Learning Constructive Scientific Paper Feedback from Author Responses

Jimin Mun, Chani Jung, Xuhui Zhou +2

While LLMs hold significant potential to transform scientific research, we advocate for their use to augment and empower researchers rather than to automate research without human…

cs.AI2026

1-2-3 Check: Enhancing Contextual Privacy in LLM via Multi-Agent Reasoning

Wenkai Li, Liwen Sun, Zhenxiang Guan +2

Addressing contextual privacy concerns remains challenging in interactive settings where large language models (LLMs) process information from multiple sources (e.g., summarizing m…

cs.AI2026

Agents of Chaos

Natalie Shapira, Chris Wendler, Avery Yen +35

We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment with persistent memory, email accounts, Discord acc…

cs.AI2026

OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety

Sanidhya Vijayvargiya, Aditya Bharat Soni, Xuhui Zhou +4

Recent advances in AI agents capable of solving complex, everyday tasks, from scheduling to customer service, have enabled deployment in real-world settings, but their possibilitie…

cs.AI2026

Social World Models

Xuhui Zhou, Jiarui Liu, Akhila Yerukola +2

Humans intuitively navigate social interactions by simulating unspoken dynamics and reasoning about others' perspectives, even with limited information. In contrast, AI systems str…

cs.AI2025

Training Proactive and Personalized LLM Agents

Weiwei Sun, Xuhui Zhou, Weihua Du +5

Despite rapid progress, current AI agents are primarily optimized for isolated task completion. We argue for a paradigm shift toward training agents as collaborators that communica…