Xiaobin Hu

JIT-Agent transforms agent performance by enabling real-time, adaptive harness evolution, leading to significant performance gains over existing models.

Achieving a staggering 54.67× speedup in text-to-video-audio generation without sacrificing quality could revolutionize real-time multimedia applications.

LLMs can significantly boost forecasting accuracy, but their effectiveness is hampered by measurement limitations and sensitivity to input changes.

ToolArtist achieves unprecedented synergy in image generation by unifying reasoning and tool use under a single agent policy, outperforming conventional methods.

Shifting the focus from static state transitions to dynamic, agent-centric feedback could revolutionize how we train and evolve intelligent agents.

DOPD reveals that intelligently routing supervision based on advantage gaps can significantly enhance capability transfer in distillation, outperforming conventional methods.

MambaADv2 achieves superior anomaly detection by combining linear computational efficiency with advanced global and local representation modeling, setting a new standard in unsupervised learning.

SPOT-E transforms frozen VLMs into more reliable evidence readers by dynamically spotlighting critical visual information during inference.

OPD-Evolver outperforms traditional memory systems by up to 11.5%, showcasing a new paradigm in agent evolution that transcends mere memory storage.

Real-time audio interaction is now possible with a unified model that not only performs traditional tasks but also proactively responds to audio stimuli.

Forget short-sighted compression: Future Forcing anticipates future query needs in autoregressive video generation, boosting long-horizon consistency by up to 1.49 on VBench-Long without any training.

Real-world proactive agents can now infer latent user needs and act on them in real-time, rivaling state-of-the-art models in intent detection while maintaining low latency.