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AI at Meta

@AIatMeta
We're building the future of personal superintelligence to help people create, connect, and achieve more in life.
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Today we published a technical report on Muse Glimmer, our 30B open-weight model built for agentic tasks on consumer hardware. It runs at practical speeds on a Mac or PC with a single consumer GPU at 4-bit precision, and leads comparably-sized open models on tool use and https://t.co/PssTwoaTAZ
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We trained our Muse foundational models, including Muse Wonder, at Prometheus, our gigawatt-scale supercluster in New Albany, Ohio. Across a supercluster with hundreds of thousands of GPUs, even one underperforming host can slow an entire training run. We engineered Prometheus https://t.co/XXCih5wrfv
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RT @natolambert: Guys this is like my superbowl I waited so long for US open weight models to be relevant again. Before today the US models…
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RT @ArtificialAnlys: Meta has released Muse Spark Open, an 816B (42B active) open-weights model that scores 39 on the Artificial Analysis I…
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RT @thehypedotnews: meta muse spark open beats 3 open-weight models – best pixel survivors game muse spark open builds the most playable…
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RT @cursor_ai: Muse Wonder from Meta Superintelligence Labs is now available in Cursor! It scores 46.1% on CursorBench at default effort,…
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We’re excited to introduce the Muse Wonder series with Muse Wonder and Muse Wonder Full, our most capable models yet. Both are frontier models, built to deliver state-of-the-art performance for personal and professional agentic tasks. Muse Wonder is available today via the Meta
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6️⃣ Non-Associative Algebra: The team worked with Muse Spark to find an exception to a proposed rule about mathematical structures inspired by biology. They went further by developing an alternative characterization, which the researchers checked and refined. Read the paper:
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5️⃣ Arithmetic Physics: Researchers working with Muse Spark connected an idea from number theory with a calculation in string theory. They proved the link works in more cases than previously known, building on ideas from the 1980s. Read the paper: https://t.co/X7IfI9bOtD https://t.co/1Bc29UArQW
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4️⃣ Optimization: When can you replace a hard math problem with a simpler one without losing anything? Researchers worked with Muse Spark to prove a clear rule for when a particular simplification captures the original exactly, and when it leaves a gap. Read the paper: https://t.co/OQzFJvo0Xj
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3️⃣ Group Theory: Researchers disproved a proposed rule about mathematical structures that describe symmetry by finding one counterexample. Muse Spark generated the search code that found it, and the team verified the result and completed the proof. Read the paper: https://t.co/MiLQnyj2gY
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2️⃣ Differential Equations: Imagine a tug-of-war between one effect squeezing a wave inward and another spreading it out. Can the wave keep concentrating forever? With help from Muse Spark, researchers proved that, under the conditions studied, a wave in a laser-inspired model https://t.co/v6xpLaAF5s
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1️⃣ Probability: Mathematicians worked with Muse Spark to answer a question about fitting random points onto the surface of a stretched sphere. For the setting studied, they proved a sharp cutoff between when an exact fit is likely and when it is unlikely. Read the paper: https://t.co/Thzx6OI7wp
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Following gold-medal-level performance from our AI models across five competitions in mathematics, physics, and chemistry, we asked a harder question: can AI contribute when a problem is genuinely open and without an existing solution path? Over the past several months,
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