MOTOR-Bench supplies a real-world video dataset for structured mental state understanding in learning settings, while MOTOR-MAS improves zero-shot prediction of behavior, cognition, and emotion labels over single models and other multi-agent systems.
MIT press, 2000
4 Pith papers cite this work. Polarity classification is still indexing.
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AffectGPT-RL applies reinforcement learning to optimize non-differentiable emotion wheel metrics in open-vocabulary multimodal emotion recognition, yielding performance gains and state-of-the-art results on basic emotion recognition benchmarks.
HormoneT5 augments T5 with a hormone-inspired block that predicts six continuous emotion values and uses them to modulate responses, reporting over 85% per-hormone accuracy and human preference for emotional quality.
MER2026 defines four tracks to advance generative emotion understanding from individual basic labels to dyadic, fine-grained, preference, and physiological scenarios.
citing papers explorer
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MOTOR-Bench: A Real-world Dataset and Multi-agent Framework for Zero-shot Human Mental State Understanding
MOTOR-Bench supplies a real-world video dataset for structured mental state understanding in learning settings, while MOTOR-MAS improves zero-shot prediction of behavior, cognition, and emotion labels over single models and other multi-agent systems.
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AffectGPT-RL: Revealing Roles of Reinforcement Learning in Open-Vocabulary Emotion Recognition
AffectGPT-RL applies reinforcement learning to optimize non-differentiable emotion wheel metrics in open-vocabulary multimodal emotion recognition, yielding performance gains and state-of-the-art results on basic emotion recognition benchmarks.
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A Hormone-inspired Emotion Layer for Transformer language models (HELT)
HormoneT5 augments T5 with a hormone-inspired block that predicts six continuous emotion values and uses them to modulate responses, reporting over 85% per-hormone accuracy and human preference for emotional quality.
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MER 2026: From Discriminative Emotion Recognition to Generative Emotion Understanding
MER2026 defines four tracks to advance generative emotion understanding from individual basic labels to dyadic, fine-grained, preference, and physiological scenarios.