There’s lots of buzz around agentic harnesses for robots, particularly for long horizon tasks that require complex reasoning and memory. But what will it really take to turn a reasoning agent into a reactive, reliable and low-latency robot policy? In our new paper, workspace models, we design a new memory architecture that acts as a latent harness for stronger reasoning models. Here’s why we think this might be the way forward: (1/9)

Out today in Cell Systems: the final version of our work on biologically-inspired models for predicting how molecules smell! With @SeyoneC and @jdthamores

Today we’re announcing r-1, our new document parsing model. It’s more accurate than our most powerful agentic OCR models, faster, and up to 6x cheaper. At @reductoai, we spent two years building specialized models for complex visual layouts, tables spanning multiple pages, and key formatting like strikethroughs. We then used everything we learned to build r-1, the first in a new generation of models designed to handle the hardest documents without multiplying cost. This early preview delivers a 20% lower error rate than our most accurate legacy agentic models and will keep improving with new checkpoints over the next few weeks. Accuracy is only the beginning. r-1 preview is available at 1¢ per page all-in, with additional volume discounts as you scale. In the near term we’re also going to release r-1 mini, and an auto mode that intelligently selects the right approach for each page. If you’re currently using another parser, we’re offering up to $5,000 in credits to evaluate and migrate. You can claim the migration offer and learn more about r-1 using the links in the comments. Happy parsing!

new uc berkeley data science building

What if the best visual reasoning steps are ones humans can’t specify? 🤔 Existing VLM reasoning is often constrained by language, pixels, and human-designed intermediates. We introduce Latent Implicit Visual Reasoning, where we show that VLMs can discover the best visual reasoning steps by themselves — no bboxes, no intermediate images, no extra supervision. Presenting this week at CVPR! (1/n)🧵