Changling Li — accepted AI-conference papers

2026

Position: Safe Models Do Not Guarantee Safe Societies: The Case for Sociopolitical Risk

David Guzman Piedrahita, Dave Banerjee, Changling Li, Terry Zhang, Kevin Blin, Samuel Simko +5

ICML 2026spotlight

Sociopolitical AI risks are threats to collective self-determination: a society's capacity to articulate its interests and realize them through institutions. We argue that sociopolitical AI risks emerge when general-purpose AI systems are integrated into society in ways that disproportionately ampli…

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2025

Agent-to-Agent Theory of Mind: Testing Interlocutor Awareness among Large Language Models

Younwoo Choi, Changling Li, Yongjin Yang, Zhijing Jin

EMNLP 2025

As large language models (LLMs) are increasingly integrated into multi-agent and human-AI systems, understanding their awareness of both self-context and conversational partners is essential for ensuring reliable performance and robust safety. While prior work has extensively studied situational awa

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