RESEARCH

How do we build a visual AI that truly understands everything from charts to complex science?

Researchers at Princeton University present Vero.

Vero is a family of fully open-source vision-language models trained with a massive 600K sample dataset (Vero-600K) from 59 diverse… pic.twitter.com/nXMfBWfijM

— 机器之心 JIQIZHIXIN (@jiqizhixin) 8 avril 2026

How do we build a visual AI that truly understands everything from charts to complex science? Researchers at Princeton University present Vero. Vero is a family of fully open-source vision-language models trained with a massive 600K sample dataset (Vero-600K) from 59 diverse datasets, along with a novel reward system. This fully open recipe makes powerful visual reasoning accessible. Vero achieves SOTA performance for open-weight models, improving 3.7-5.5 points across 30 benchmarks on average. It even outperforms Qwen3-VL-8B-Thinking on 23 benchmarks without proprietary thinking data, excelling in spatial reasoning, STEM, chart interpretation, and more.

→ View original post on X — @jiqizhixin