Stanford AI Lab

@StanfordAILab

The Stanford Artificial Intelligence Laboratory (SAIL), a leading #AI lab since 1963. ⛵️🤖 Emmy-winning video: youtube.com/watch?v=Cn6nmW…

Stanford, CA

ai.stanford.edu

Joined November 2018

332 Following

279.5K Followers

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Stanford AI Lab
@StanfordAILab
Nov 6
Check out our new SAIL blog post on ScholarCatalyst, a reasoning-intensive retrieval benchmark for scientific discovery built from human researchers’ judgments. The authors rethink retrieval as research intuition and explore how it can surface promising directions for open-ended discovery. ai.stanford.edu/blog/scholarca…
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Stanford AI Lab
@StanfordAILab
Nov 5
More @StanfordAILab grads hard at work!
Sabri Eyuboglu
@EyubogluSabri
Nov 4
A student takes notes for an open book exam and shares them with a friend. They both use the notes during the exam, yet only the one who made it aces Similarly, for language models, neural and text memory stack
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Engram
@EngramLab
Nov 4
In our new blog, we share some new science investigating how RL complements text-based memory by letting models learn how to use their notes. It turns out that training can be cheap compared to building notes, and stays surprisingly robust even through millions of tokens of
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Stanford AI Lab
@StanfordAILab
Nov 4
@StanfordAILab’s @talaei_shayan shipping incredible work! 🚀
Shayan Talaei
@talaei_shayan
Nov 4
Really happy to finally share Ripple-65B Preview! We trained it from scratch with a core team of five researchers, and it sets a new U.S. open-weight SOTA on Terminal-Bench 2.1 for agentic coding. Three choices that shaped how we built it, and a few things I learned along the
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Ricursive Intelligence
@RicursiveAI
Nov 4
Today we release Ripple-65B Preview, our 65B dense LLM we pretrained from scratch. Ripple sets a new state-of-the-art for U.S. open-weight models in agentic coding tasks, surpassing significantly larger models including Muse Spark Open-816B, Beam-501B, Inkling-975B, and Nemotron
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Stanford AI Lab
@StanfordAILab
Nov 4
Check out Joules to GDP, which makes it possible for us to measure the economic value per unit of energy spent on compute!
Jon Saad-Falcon
@JonSaadFalcon
Nov 4
AI is using more and more of the world’s energy. But how much economic value are we getting from AI? We’re introducing Joules-to-GDP (J2G), a framework that measures how well AI turns energy into economic value and shows which tasks, models, and hardware give the best return.
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Stanford AI Lab
@StanfordAILab
Nov 4
@StanfordAILab faculty building incredible open models out in the wild! Congratulations to the @RicursiveAI team!
Ricursive Intelligence
@RicursiveAI
Nov 4
Today we release Ripple-65B Preview, our 65B dense LLM we pretrained from scratch. Ripple sets a new state-of-the-art for U.S. open-weight models in agentic coding tasks, surpassing significantly larger models including Muse Spark Open-816B, Beam-501B, Inkling-975B, and Nemotron
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