The interesting part isn’t just open-vocabulary detection. It’s treating perception as an iterative process—looking closer when needed, building evidence, and adapting to the task instead of relying on a fixed detector.
Despite having vision, most AI agents still struggle to see. General-purpose multimodal models are powerful, but they’re expensive for every visual task. We built something better: Perceptron's MCP gives any agent stronger vision capabilities through Isaac with far lower cost.
Data curation methods collapse quality into a single score, but quality is multidimensional. We are excited to share SkillRater — a multimodal extension to DataRater. By decomposing filtering into capability-aligned raters, we outperform monolithic scoring across all dimensions with near-orthogonal learned signals
Today we’re open-sourcing a preview of our two new models in the Isaac family: hybrid-reasoning 2B and 1B-parameter best-in-class vision-language models.
Weights → https://t.co/1WgHMDfCST
Blog → https://t.co/8MOLPKpUhO
Demo → https://t.co/sAKt5dnZ6U
Perceptron’s platform is here — built for Physical AI
Developers can now use Isaac-0.1 or Qwen3VL 235B via:
Perceptron API — fast, reliable multimodal intelligence
Python SDK — simple, grounded prompting for vision + language
Build apps that see and understand the world.
Today we’re releasing the technical details behind Isaac 0.1. We built Isaac to demonstrate that with the right principles, a simple recipe can reach competitive performance with a small model.
Check out the report at: https://t.co/Y1Ys2NHswX
Designing multimodal systems is challenging. To build with the community we’re sharing the design behind TensorStream - a convenient tensor-like interface for interleaved multimodal data. Internally our training and inference code is built on top of this. https://t.co/qnkePqXhy6
Super excited to share what we've been up to at @perceptroninc Today we've released Isaac 0.1 — our first perceptive-language model: 2B params, but highly capable. Check it out: https://t.co/ohlQOUwxm3
1/ Introducing Isaac 0.1 — our first perceptive-language model. 2B params, open weights. Matches or beats models significantly larger on core perception. We are pushing the efficient frontier for physical AI.
https://t.co/dJ1Wjh2ARK
Highly recommend the https://t.co/bdkVi6pbc3 weekly summary to keep up with the field of ML. They're using AI to identify the top publications and breakthroughs, it's a game-changer.
Honored that our 12-year-old work at Microsoft on incorporating user behavior signals into ranking (with Susan Dumais and Eric Brill) was recognized with SIGIR ToT award. Also, happy to see that 12 years later there is still lots of room for improvement :-) https://t.co/yAjHxcSiUm
Ahmed Hassan Awadallah and I have one open research internship position this summer. If you're interested, please let us know and kindly share your CV!
I am humbled to receive an award named in honor of Karen Spärck Jones, whose contributions continue to be relevant to information retrieval researchers and engineers. https://t.co/SSgCKhZLux