Jin Su

The paper proposes CSMR, a cognitive scheduling framework that allows a language model to dynamically decide when to acquire task-relevant visual evidence, significantly improving multimodal reasoning accuracy.

CobSeg introduces a multi-branch architecture that enhances dialogue topic segmentation by explicitly modeling both semantic coherence and local lexical boundary transitions, achieving state-of-the-art performance without relying on large language models during inference.

The paper introduces TRON, an online, rule-verifiable environment substrate that generates an unbounded stream of fresh, controllable visual reasoning training instances, significantly improving RL performance on external multimodal benchmarks.

The paper introduces Label-Augmented Conditional Diffusion (LACD), a method for learning complex conditional distributions using unlabeled data, and provides theoretical guarantees for its effectiveness.