Yulun Zhang

SJRL not only overcomes collision challenges in multi-agent navigation but also adapts dynamically to real-world constraints, outperforming traditional methods in complex environments.

ET-Prune achieves a remarkable balance between efficiency and accuracy, outperforming traditional pruning methods by retaining critical evidence while cutting down on unnecessary tokens.

SRA can double the operational capacity of automated warehouses while slashing optimization time from hours to minutes.

Error accumulation in long video generation can be mitigated with a novel frequency-domain approach, achieving 24x extrapolation without the need for extensive training.

Binarization methods that ignore weight significance can lead to substantial performance losses, but SAB-LVLM optimizes this process, achieving superior efficiency without sacrificing accuracy.

Structured supervision can enable 3B VLMs to outperform 32B VLMs on dense-scene reasoning tasks, suggesting a path to efficient and reliable visual understanding.

Pushing super-resolution models to the extreme of 2-bit quantization doesn't have to mean sacrificing accuracy, thanks to QuantSR+'s clever combination of quantization-aware operators, architecture, and training.