Chen Li

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.

PaDoc achieves a remarkable 94.24 F1 score among end-to-end parsers while being the fastest parser at five concurrency levels, revolutionizing document parsing efficiency.

Surpassing larger models, this agent achieves 91.4% retrieval accuracy in long-horizon multimodal dialogues by leveraging episodic memory for efficient context management.

Audio-Omni can edit sound, music, and speech with a single model, rivaling specialized systems and unlocking capabilities like knowledge-augmented reasoning and zero-shot cross-lingual control.