HumanVBench provides a 16-task benchmark for human-centric video understanding in MLLMs, created through automated annotation and distractor synthesis pipelines, and shows top models lag human performance on emotion perception and cross-modal alignment.
Llava-next: Im- proved reasoning, ocr, and world knowledge
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VLM-R1 applies R1-style RL using rule-based rewards on visual tasks with clear ground truth to achieve competitive performance and superior generalization over SFT in vision-language models.
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VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model
VLM-R1 applies R1-style RL using rule-based rewards on visual tasks with clear ground truth to achieve competitive performance and superior generalization over SFT in vision-language models.