Xing Sun

This work formalizes a Wiki Graph schema that seamlessly bridges fine-grained structures with dense contexts, maintaining explicit topologies alongside continuous semantics and engineer an infrastructural NCCL boundary exchange protocol that hoists static partition indices and leverages fixed-shape GPU-to-GPU collectives, bypassing CPU serialization and memory copy overheads.

Expert artifacts can serve as a goldmine for reconstructing evidence-seeking trajectories, leading to substantial improvements in long-form generation tasks.

Models that appear equally accurate can possess vastly different agentic reasoning capabilities, revealing the hidden complexities of LLM performance.

Latent-OPD reveals that distilling latent representations at trajectory endpoints can dramatically enhance video reasoning efficiency in LMMs.

Achieving a 3.56× increase in decoding throughput without sacrificing accuracy, BinaryPC revolutionizes efficiency in long-context LLMs.

Categorical value learning can significantly enhance the performance of PPO critics in reinforcement learning, leading to better calibration and lower variance in advantage estimation.

TopoAgent's innovative use of dynamic graph evolution allows for noise-resistant scientific reasoning that outperforms traditional linear models.