human-ai-coordination

TRACE is a new multi-layer benchmark for diagnosing drift and failures in human-AI controller coordination, built from ALFRED traces with 1,918 drifted samples annotated across five execution layers. Baseline results show drift identification and attribution well above random baselines across classical, recurrent, and attention-based model families.

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.