Peng Gao

Robots can now maintain accurate relative pose estimates even when visual overlap is absent, thanks to a novel communication-efficient framework.

A Supervisor Agent can mediate conflicts in LLM outputs, leading to substantial accuracy improvements in e-commerce catalog enrichment.

Z-Image Turbo++ achieves high-quality 2-step image generation that rivals 8-step models, transforming the landscape of diffusion distillation.

Z-Reward achieves 41.3% better human preference alignment in text-to-image generation by transforming complex reasoning into efficient score distributions.

MiniMax-M2 proves that massive parameter counts don't always translate to better agentic performance; strategic activation of a smaller subset can unlock frontier-level intelligence.

LLMs can finally extract cyberattack patterns from threat reports with high precision, thanks to a novel "diverge-then-converge" approach that mimics how human analysts validate findings.

Fine-tuning efficient few-step diffusion models no longer requires sacrificing their speed, thanks to a self-distillation approach that preserves inference capabilities.

LLMs can't even reproduce published physics papers end-to-end, with the best model scoring only 34% on a new benchmark designed for this purpose.

Image protection schemes are surprisingly brittle: off-the-shelf image-to-image models can bypass them with a simple "denoise" prompt, often outperforming specialized attacks.

Dataset distillation can be sped up by 18x on ImageNet-1K without sacrificing accuracy by focusing optimization on high-loss regions.

PANC achieves a staggering 162% increase in attack effectiveness against Transformer-based visual trackers while slashing noise levels to just 10%.