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.
Affectgpt: A new dataset, model, and benchmark for emotion understanding with multimodal large language models
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EmoTrans is a new video benchmark with four progressive tasks that measures how well current multimodal LLMs handle dynamic emotion transitions rather than static recognition.
Introduces UXBench benchmark for MLLM UI UX reasoning and UI-UX model achieving 0.7963 accuracy via RL enhancements on Qwen3-VL base.
DeceptionX is an MLLM framework that performs explainable deception detection through structured chain-of-thought reasoning on audiovisual cues, trained via a three-stage pipeline on the new DeceptChain dataset and a DARE redundancy elimination strategy.
EmoS is a new high-fidelity benchmark for fine-grained streaming emotional understanding that produces measurable gains when used to fine-tune multimodal large language models.
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Reasoning for Mobile User Experience with Multimodal LLMs: Task, Benchmark, and Approach
Introduces UXBench benchmark for MLLM UI UX reasoning and UI-UX model achieving 0.7963 accuracy via RL enhancements on Qwen3-VL base.