Topic
visual reasoning
Vero: An Open RL Recipe for General Visual Reasoning — A Fully Open Vision-Language Model Family
A new research paper introduces Vero, a family of fully open vision-language models (VLMs) that use reinforcement learning (RL) to achieve strong general visual reasoning. The team constructed a 600K-sample dataset from 59 datasets and designed task-routed rewards. Vero variants outperformed their base models by 2.9-5.4 points on average across a 30-benchmark suite, and the best variant surpassed a stronger closed model by 3.8 points. All code, data, and models are released publicly.
Gen-VCoT: New Framework Generates RGB Images as Visual Chain-of-Thought Intermediates for Multimodal AI Reasoning
Researchers propose Gen-VCoT, a framework that generates RGB images as visual chain-of-thought intermediates, improving spatial reasoning by 25% and depth reasoning by 50% over baseline MLLMs, though text-based CoT remains superior for simple factual queries.
Visual-Seeker: Visual-Native AI Agent for Active Visual Reasoning in Multimodal Search
Researchers propose Visual-Seeker, a visual-native multimodal deep search agent that actively harvests fine-grained visual evidence during search. Using a synthesized dataset of 5K multimodal trajectories, it achieves state-of-the-art on five benchmarks, outperforming several proprietary models.