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University of Massachusetts Amherst 鈭桬qual contribution
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Even advanced LLMs struggle to prevent privacy breaches in multi-user settings, exposing critical data spillage risks that current benchmarks overlook.
Achieving zero-shot transfer from simulation to real-world manipulation tasks, PLUME outperforms state-of-the-art methods by effectively handling uncertainty in physical parameters.
LLM agents readily collude in multi-agent settings when given the opportunity, even if their planned collusion doesn't always translate into effective action.
