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Recurrent Longitudinal Memory (ReLMem), a framework that learns to maintain fixed-capacity patient memory for efficient downstream prediction with a frozen LLM, is introduced and a multi-granularity optimization strategy to preserve task-relevant information throughout recurrent updates and support downstream predictio...
CDEG, a graph-based framework that learns reusable decision-critical evidence from historical diagnostic trajectories, is introduced, demonstrating that reliable long-horizon diagnosis requires moving beyond trajectory-level experience reuse toward evidence-level learning of the factors that truly shape clinical decisi...
These findings establish articulated rationales as a useful complement to behavioral and content signals, and demonstrate a practical role for MLLMs in scaling sparse human explanations into preference information that improves industrial recommendation.
This work proposes TARS, a 3D-free video re-shooting paradigm that provides more accurate and temporally consistent camera control than prior methods, and introduces self-supervised training to learn camera dynamics and fundamental visual representations without paired re-shooting data or 3D reconstruction.
DentVLM is a dental vision-language model developed to support dental diagnosis across seven oral imaging modalities and 36 tasks that surpasses junior readers, matches intermediate general practitioners and approaches senior specialists, and reduces diagnostic time by 15.0-37.0% in collaborative clinical workflows.
Video motion transfer aims to animate a target object using dynamics from a reference video. Existing formulations largely rely on fixed structural correspondence, which becomes ill-defined when reference and target objects differ substantially in morphology, articulation, or deformation mechanisms. We introduce Motion...
This work develops MedUAG, an end-to-end trained unified medical model that achieves strong performance across a wide array of understanding and generation tasks, establishing a competitive baseline and paving the way for next-generation medical multimodal systems.
DentAgent is introduced, an evidence-centric multi-agent framework, in which the Orchestrator coordinate five specialized agents spanning various modalities, which supports its value for broadly applicable and traceable multimodal dental reasoning, and highlights its potential as a technical foundation for population o...
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