At its core, @cohereforai.bsky.social is about advancing open science, exploring fundamental research questions, and sharing knowledge broadly

always guided by the pleasure of finding things out.

Excited for what’s next!

Very grateful to @sarahooker.bsky.social for shaping the lab into what it is today, and looking forward to working with Joelle whose guidance will be invaluable as we write our next chapter. Also big thanks to @nickfrosst.bsky.social
and @aidangomez.bsky.social Ivan for their support and trust.

If you've been waiting for your moment to step into research, this could be it.

cohere.com/research/sch...

Over the years, I've watched scholars go from their very first project → to their first paper → to research careers they once thought were out of reach.

It’s been incredible to see what can happen when someone gets their first real chance and works hard to make it count 🏅

There’s so much open space here: how to better integrate language nuances, how to curate and align cross-modal data well, how to go beyond six languages.
Excited to keep exploring it and even more excited to see what the community builds on top of this 💫

Huge kudos to my collaborators: @mmderakhshani.bsky.social Dheeraj Varghese @cgmsnoek.bsky.social 🙌

We’re releasing the model, data, and evals soon to help make multilingual image generation more robust, inclusive, and reproducible.

neobabel.github.io
arxiv.org/abs/2507.061...

One of my favorite parts of NeoBabel is multilingual inpainting & extrapolation: you can mask part of an image generated in language A, prompt it in language B, and it fills in the scene naturally—no special tuning needed.

We used a multistage training setup: starting from class-label grounding, then scaling up to massive multilingual image-text pairs, and finally instruction tuning with high-res, diverse prompts.

This helped the model gradually learn structure, language, and fine-grained control.

We put a lot of effort into building a clean, well-aligned multilingual dataset (124M image-text pairs across 6 languages) and it paid off.

NeoBabel generates well in every language. And it’s only 2B params—beating much larger models on benchmarks.

This was my first project in image generation, and coming from language I was shocked at how little care is given to text quality in many vision datasets.
Captions are often noisy, shallow, or poorly formatted.

I guess your next challenge is making veo-baby as cute as your actual baby 😍