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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.
WeVisDoc is presented, a two-stage data-centric framework for robust end-to-end document parsing that broadens semantic, structural, and appearance coverage through heterogeneous data and structure-preserving degradation synthesis and guides targeted data construction and reallocation of the target-token budget.
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.
PaDoc is proposed, a layout-grounded parser that treats the predicted layout as a branching structure over a shared page representation that is the fastest end-to-end parser at five concurrency levels and is the fastest end-to-end parser at five concurrency levels.
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