Dian Zheng

A unified multimodal agent, OneSearch-VL, achieves state-of-the-art performance on diverse visual research tasks by explicitly modeling and preserving interdependencies within a Visually Grounded Evidence Graph.

InterleaveThinker transforms standard image generators into powerful tools for interleaved generation, achieving state-of-the-art performance while enhancing reasoning capabilities.

Image generation takes a leap towards real-world knowledge by training an agent that actively searches for and integrates external information, substantially boosting performance on knowledge-intensive tasks.

LongCat-Next shatters the language-centric paradigm by unifying text, vision, and audio into a single autoregressive model with minimal modality-specific design, finally reconciling understanding and generation in discrete vision modeling.