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JarvisBench introduces a benchmark for evaluating the coordination between humans and AI agents, focusing on attention allocation in long-horizon tasks. It provides a reference implementation with a full-duplex speech interface.
This paper proposes an attention-guided decision framework for hospital pharmacists managing drug shortages, modeling bounded rationality by dynamically decomposing drugs into urgent and monitoring subsets, and shows that selective attention enables stable decision-making without full state reasoning.