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This paper investigates how AI agents can learn explicit social norms from human behavior to improve coordination in dynamic interactions, using pedestrian-vehicle scenarios as a testbed. The proposed norm-informed LLM outperforms baselines and human-human interactions by a significant margin.
Introduces CCBench, a framework for evaluating LLMs' cultural competence via health queries with personas across six cultures, finding that even top models achieve only 20-30% culturally appropriate responses.