fig

@figbrains

hyperintelligence for humanity

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fig.inc

Joined January 2024

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fig
@figbrains
Oct 31, 2025
We're excited to announce MultiNet v1.0 - the first cross-domain benchmark for multimodal AI systems. Unlike existing evaluations that test models within single domains, MultiNet reveals what happens when AI systems encounter the full complexity of real-world tasks.
fig
@figbrains
Aug 26
Try your hand at beating AI on maze solving in our new evaluation preview!
harsh✌️
@HarshSikka
Aug 26
You can solve simple mazes like this in 1 minute. Frontier models can do that too...right? While designing a new frontier evaluation, we were surprised to find complete failure on basic 2D mazes. Excited to share an early research preview, playable demo, and highlights below!
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fig
@figbrains
Jun 16
GUI-DR applies domain randomization from robotics, varying visual scenes and instructions along controlled axes to expose fragile model behaviors.
harsh✌️
@HarshSikka
Jun 16
Computer Control models can score 90%+ on standard benchmarks, but will fail when you set page zoom to 70%. We're built GUI-DR, an OS pipeline that can restyle, reposition, and remove DOM elements on real webpages to reveal model weaknesses that fixed-scene benchmarks miss.
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fig
@figbrains
Jun 5
Fig wants to directly support researchers working on foundationally new takes on frontier models - targeting hard problems like long horizon multi-environent action. Reach out to contact @ fig . inc if you're working on these or related areas.
Pranav Guruprasad
@pranavguru13
Jun 5
This week at #CVPR2026 we presented MultiNet v1.0 at the MMFM workshop. It is a benchmark built around a question most evaluations skip: what happens to a multimodal model when you take it out of the one domain it was trained for and ask it to handle everything at once?
fig
@figbrains
Jun 3
Come meet the Fig team @ CVPR this week, today through Friday!
Pranav Guruprasad
@pranavguru13
Jun 2
Headed to #CVPR2026! I'll be there on behalf of @figbrains and @ManifoldRG, presenting our research on next-generation multimodal models and evaluation systems. If you're into multimodal models, VLAs, or how we actually evaluate them, come say hi - I'd love to talk!