H2HMem is a multimodal memory benchmark evaluating LLM agents on recall, reasoning, and application in dyadic and multi-party human-human conversations with phenomena such as anaphora and deixis.
Fast multi-party open- ended conversation with a social robot.arXiv preprint arXiv:2503.15496, 2025
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A study with 24 groups finds LLM intervention explanations in multi-party HRI emphasize facilitation, agreement, and flow, with stable patterns across conditions but role-based differences between mover and opposer robots.
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H2HMem: A Multimodal Memory Benchmark for Agents in Human-Human Interactions
H2HMem is a multimodal memory benchmark evaluating LLM agents on recall, reasoning, and application in dyadic and multi-party human-human conversations with phenomena such as anaphora and deixis.
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Understanding LLM Intervention Explanations in Multi-Party Human-Robot Interaction
A study with 24 groups finds LLM intervention explanations in multi-party HRI emphasize facilitation, agreement, and flow, with stable patterns across conditions but role-based differences between mover and opposer robots.