Introducing Vero, the strongest fully open RL recipe for training next-generation visual reasoners. From charts to spatial to open-ended tasks, Vero sets a new bar. • sota 8B VLM across 30 benchmarks • 4.4 avg over four base models (30 evals) • beats prior RL datasets 🧵👇

It’s clear VLMs still struggle across diverse visual inputs. Introducing WorldBench, our new VLM benchmark with visually diverse, realistic, and challenging VQA questions! All questions are annotated *by hand* and checked meticulously for quality. Try it out for yourself!

Great to see this level of transparency from an industry lab. Detailed recipes and reporting like this are a real asset to the research community and the public!

I’ll be at CVPR June 3-6 and excited to talk with folks! We’ll be presenting Vero at June 3 workshops (vero-reasoning.github.io): - DataMFM Oral 2:40pm, Room 111 - MMFM Poster, 3pm, 3A - ViScale Poster, 5pm, 506 And be sure to come by the CogVL Workshop cogvl.github.io!

CogVL Workshop at #CVPR2026 is less than a week away! We have an exciting lineup of keynote speakers across Vision, NLP and Cog Sci, and orals/posters on reasoning methods for VLM models. 🕐 June 3, 1 PM 📍 Rooms 610/612 🔗 Schedule: cogvl.github.io

Happy to be recognized as an outstanding reviewer at CVPR!

Really excited about this new work scaling VLMs to 100 turns for playing Mario! Games are a great testbed for long-horizon agentic tasks, and this setting makes it clear the default short-horizon tricks for VLM training often fall short. Check out the thread and paper!

AI agents can work pretty well on the web now for short tasks. I wanted to know: could they go longer, on harder tasks? Can an agent plan 2 weddings in different cities and a honeymoon within the same month, or find the most suitable culinary arts school across the US for my post PhD plans? We are releasing Odysseys: a benchmark of 200 long-horizon web agent tasks evaluated on the live internet. All our tasks are inspired from real human data and many take hours to complete. The best frontier model we tested (Claude Opus 4.6) reaches only 44.5% perfect-task success, leaving substantial room for improvement. I donated a couple of my own automation wishes to this benchmark. My favorite contribution was to “Rank the top 10 ACL Meniscus surgeons in the area” - as this took me a decent amount of time to do myself when I got my own knee fixed. GPT 5.5 was able to do this for me with 4 dollars and 30 minutes!

Hi all. I just put up my course materials for my Multimodal LLM Agents course at @UUtah online so it can be used by the broader community. I really tried to tie together LLMs with RL and CV to give students the full picture of how MLLMs can be incorporated into embodied agents.

Must-read research of the week ▪️ Neural Computers ▪️ The Illusion of Stochasticity in LLMs ▪️ Learning is Forgetting: LLM Training as Lossy Compression ▪️ A Frame is Worth One Token: Efficient Generative World Modeling with Delta Tokens ▪️ INSPATIO-WORLD: A Real-Time 4D World Simulator via Spatiotemporal Autoregressive Modeling ▪️ Vero: An Open RL Recipe for General Visual Reasoning ▪️ RAGEN-2: Reasoning Collapse in Agentic RL ▪️ TriAttention: Efficient Long Reasoning with Trigonometric KV Compression ▪️ In-Place Test-Time Training ▪️ Fast Spatial Memory with Elastic Test-Time Training ▪️ Gym-Anything: Turn any Software into an Agent Environment ▪️ SkillClaw: Let Skills Evolve Collectively with Agentic Evolver ▪️ PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing Find all the links and other important AI news of the week here: turingpost.com/p/fod148

aimodels.fyi/papers/arxiv/ve… > be me > open source researcher > everyone making amazing vision AI but won't share how > "just trust me bro it works" > decide to build our own and document everything > call it Vero > actually works > tfw transparency wins > recipe.pdf

My biggest takeaway from this project: even in an academic setting, with the right people and dedication, we can catch up with part of what top industry teams have achieved. Vero's final training run only takes 8 GPUs. Vero only addresses the RL reasoning stage, but it proves that a lot is still very much possible in academia!

Today we release Vero for visual reasoning. Open data, open training code, and open weights! Vero is the strongest open-weight 8B VLM on average across 30 benchmarks. Vero reasons broadly across six diverse visual domains, from charts and science to spatial understanding and open-ended tasks.