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Interpolated Policy Distillation is introduced, which defines the next-token distribution at every decoding step as an explicit linear interpolation between the student and teacher distributions, demonstrating that token-level policy interpolation better balances trajectory quality and student learnability.
WeMM-Embedding is presented, a family of universal multimodal embedding models supporting text, images, videos, visual documents, and arbitrarily interleaved multimodal inputs with flexible output dimensions and achieves leading performance on multiple public benchmarks.
MMAgent-R$^2$, an agentic mRAG framework that integrates visual reranking and active rejection as its internal verification mechanism, is proposed and achieves joint optimization of external retrieval, internal verification, and answer generation via GRPO training.
This paper proposes SegAnswer, which shifts the unit of zoom-in from the popular bounding box to pixel-level segmentation mask, and demonstrates its capability for reliable pixel grounding.
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