
Research Scientist @googledeepmind, Gemini omni | past: founding researcher @reve and Google brain
Mountain View, CA
Joined July 2016
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This is full of insights!
Yes, hardware lottery is not when your research ideas are hardware friendly by accident, you should actively design for it.
Replying to @AndriusStepona1 @teortaxesTex
The evolution of interleaved SWA and global layers with window size 512 happened due to merging Routing Transformer (github.com/google-research/g…) with scan layers - XLA likes repeated blocks to reduce compilation time. Doing top-k sparse attention proved to be harder on TPUs and most of the successful sparsification has happened on GPUs (deepseek, minimax etc).
Argon multi modal is 👑
Congrats to @GoogleAI's Gemini 4 Argon for taking the top spot of our Humanity's Sixth Sense (HSS) benchmark with a score of 55.4%, edging out GPT-6-Astra at 53.6%. 🥇
HSS tests AI’s ability to reason beyond what’s visible, from hidden objects to predicting what happens next and understanding human behavior.
We took a closer look at performance across categories, and that’s where things get interesting. 👀🧵
Dont mess with @zacharynado ✨
Zachary has become my favorite google employee in just 2 dayss..
He was underground for almost a year, probably just bookmarking posts and from the moment Gemini 4 Argon launched.. he has been ruthless in calling out every negative post..
I just hope that when we get hands-on access to the model.. all the call outs stay valid!!
Great job @zacharynado and good luck!!
Long context has been a first class citizen problem from the early days of Gemini and not surprisingly Gemini 4 is amazingly good at it. Not only at ingestion but also with long seq generation. It has been a joy seeing Argon working on long horizon and complex tasks.
Gemini 4 is a really amazing model. I have been using it internally for a while, specially for AI research and infra work ✨✨✨✨
Come join us and work on omni modality🚀
We are hiring research scientists and engineers🧑🔬 on the Gemini Omni team! Both in the US (Mountain View / SF) and Europe (London / Zürich).
Apply here: google.com/about/careers/app…
Lukasz was leading RL work in brain research back then when transformers were 200m params. It was a privilege seeing his work up close.
Let me take this opportunity to pay tribute to the RLSlow team :) It was a priviledge to lead it through the years, first with @ilyasut then with @merettm and finally on my own - the best team you can ever wish for. It feels like ages ago when we started working on the foundations of reasoning (later berry) with @TrapitBansal, Francis Song, @_smileyball and @sidorszymon.
Many more things happened since then, huge thank you to all teammates through the years: Peter Zhokhov, @unsafePerformIO @vladi_petroff @masonmeyer_ @afrozenator @lukasz_kondr @dmdohan @wang12tao @TheRealRPuri @edwardjhu @eqhylxx @suchirbalaji and Dan Selsam for all the help.
I hope this great technical work continues to be truly and broadly beneficial.
xitter.cf/merettm/status/2096630…
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Same experience, really capable model for day to day coding/research tasks
If you think looping transformers are "new", I refer you to @m__dehghani et al amazing work on universal transformers, published shortly after the transformer paper itself came out. arxiv.org/abs/1807.03819
If you think SWA's effective receptive field is num_layers*sliding_window_size I have some bad news for you
Lots of cool research has gone into gemini flash 3.7, I really enjoy using it with @antigravity. Blazing fast and great context handling.
Mohammad Saffar retweeted
it’s surprising to me how many people seem to not understand that great models are built with super high quality curated data
finding novel ways to create / get this data is a huge edge
It was a blast working on building Gemini Omni!
















