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.
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XR Blocks supplies an LLM-optimized Reality Model and Vibe Coding XR workflow that converts high-level prompts into working physics-aware XR applications with high one-shot success.
FashionStylist is an expert-annotated benchmark dataset that unifies outfit-to-item grounding, completion, and evaluation tasks for multimodal large language models in fashion.
Introduces the MMH dataset collected via psychology-inspired multimodal stimuli and a paradigm-aware framework that uses inter-disorder prior knowledge as prompts, outperforming baselines on differential detection of depression, anxiety and schizophrenia.
Integrates safety filtering and constitutional AI into FedLLM, reporting over 20% safety improvement on AdvBench.
citing papers explorer
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EmoTrans: A Benchmark for Understanding, Reasoning, and Predicting Emotion Transitions in Multimodal LLMs
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.
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Vibe Coding XR: Accelerating AI + XR Prototyping with XR Blocks and Gemini
XR Blocks supplies an LLM-optimized Reality Model and Vibe Coding XR workflow that converts high-level prompts into working physics-aware XR applications with high one-shot success.
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FashionStylist: An Expert Knowledge-enhanced Multimodal Dataset for Fashion Understanding
FashionStylist is an expert-annotated benchmark dataset that unifies outfit-to-item grounding, completion, and evaluation tasks for multimodal large language models in fashion.
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Differential Mental Disorder Detection with Psychology-Inspired Multimodal Stimuli
Introduces the MMH dataset collected via psychology-inspired multimodal stimuli and a paradigm-aware framework that uses inter-disorder prior knowledge as prompts, outperforming baselines on differential detection of depression, anxiety and schizophrenia.
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Responsible Federated LLMs via Safety Filtering and Constitutional AI
Integrates safety filtering and constitutional AI into FedLLM, reporting over 20% safety improvement on AdvBench.