GRASP is a large-scale dataset and benchmark for social reasoning grounded in gaze and gesture events in multi-person videos, with Social Grounding Reward (SGR) proposed to improve model performance on GRASP-Bench.
Towards social ai: A survey on understanding social interactions
4 Pith papers cite this work. Polarity classification is still indexing.
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SocialDirector uses spatiotemporal actor masking and directional reweighting on cross-attention maps to reduce actor-action mismatches and improve target-directed interactions in generated multi-person videos.
SAVOIR combines prospective expected utility valuation with Shapley values for fair credit assignment in social dialogue RL, achieving SOTA on SOTOPIA where a 7B model matches or exceeds GPT-4o and Claude-3.5-Sonnet.
US adults report already encountering socially intelligent AI agents judged by observable behaviors, show greater support for others' use than personal adoption, and express concerns about appropriate roles and risks.
citing papers explorer
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GRASP: Learning to Ground Social Reasoning in Multi-Person Non-Verbal Interactions
GRASP is a large-scale dataset and benchmark for social reasoning grounded in gaze and gesture events in multi-person videos, with Social Grounding Reward (SGR) proposed to improve model performance on GRASP-Bench.
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SocialDirector: Training-Free Social Interaction Control for Multi-Person Video Generation
SocialDirector uses spatiotemporal actor masking and directional reweighting on cross-attention maps to reduce actor-action mismatches and improve target-directed interactions in generated multi-person videos.
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SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution
SAVOIR combines prospective expected utility valuation with Shapley values for fair credit assignment in social dialogue RL, achieving SOTA on SOTOPIA where a 7B model matches or exceeds GPT-4o and Claude-3.5-Sonnet.
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When Should AI Read the Room? Public Perceptions of Social Intelligence in AI Agents
US adults report already encountering socially intelligent AI agents judged by observable behaviors, show greater support for others' use than personal adoption, and express concerns about appropriate roles and risks.