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Six papers from mathematicians’ collaboration with Meta’s Muse Spark on open problems

Meta says mathematicians used Muse Spark’s Thinking Mode through the regular meta.ai chat interface, without a custom research scaffold.

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2 Sources, 1d ago, first seen 1d ago

TLDR

Meta shared six papers from mathematicians’ work with Muse Spark 1.1 and 1.2 on open problems. Meta says mathematicians guided the research, a second group reviewed the work, and each paper marks passages drafted primarily by humans or AI. The papers also credit earlier research and acknowledge teams that independently announced solutions to the same problems.

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2 Sources, first seen 1d ago

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Combined views

301.1K

2 Sources, first seen 1d ago

1.1K likes83 comments348 saves142 reposts

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@AIatMetaFollowing 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, mathematicians worked with Muse Spark 1.1 and Muse Spark 1.2 in Thinking Mode through the regular http://meta.ai chat interface, with no custom research scaffold, to find solutions to such problems. Our goal wasn't to mass-produce papers, but empower researchers. Every collaboration followed the same principles: mathematicians guided the research, a second group of mathematicians then reviewed the work, each paper marks which passages were drafted primarily by humans or AI, and each credits the prior research it builds on. Where other teams independently announced solutions to the same problems, we acknowledge their work as well. Today, we're sharing six papers from that collaboration. 🧵👇1d
@SciTecheraMathematics is entering its singularity moment. AI IS STARTING TO DO SOMETHING MUCH BIGGER THAN SOLVING BENCHMARKS. > META researchers used Muse Spark 1.1 and 1.2 to work with mathematicians on six research papers, with five addressing previously open research questions. > One collaboration focused on evolution algebras, a mathematical framework inspired partly by evolutionary biology.👀 > Muse helped researcher Andres Barei discover a 3D counterexample to a conjecture proposed by García-Martínez and Pérez-Rodríguez. But it didn't stop there. AI helped explore new proof ideas and alternative ways to understand the problem. That led to a new characterization based on idempotent subspaces. Human mathematicians then also checked, refined and verified the final results. Although I'm very excited to see more fundamental physics problems being solved with AI.👀23h
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    2 Sources

    @AIatMetaFollowing 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, mathematicians worked with Muse Spark 1.1 and Muse Spark 1.2 in Thinking Mode through the regular http://meta.ai chat interface, with no custom research scaffold, to find solutions to such problems. Our goal wasn't to mass-produce papers, but empower researchers. Every collaboration followed the same principles: mathematicians guided the research, a second group of mathematicians then reviewed the work, each paper marks which passages were drafted primarily by humans or AI, and each credits the prior research it builds on. Where other teams independently announced solutions to the same problems, we acknowledge their work as well. Today, we're sharing six papers from that collaboration. 🧵👇1d
    @SciTecheraMathematics is entering its singularity moment. AI IS STARTING TO DO SOMETHING MUCH BIGGER THAN SOLVING BENCHMARKS. > META researchers used Muse Spark 1.1 and 1.2 to work with mathematicians on six research papers, with five addressing previously open research questions. > One collaboration focused on evolution algebras, a mathematical framework inspired partly by evolutionary biology.👀 > Muse helped researcher Andres Barei discover a 3D counterexample to a conjecture proposed by García-Martínez and Pérez-Rodríguez. But it didn't stop there. AI helped explore new proof ideas and alternative ways to understand the problem. That led to a new characterization based on idempotent subspaces. Human mathematicians then also checked, refined and verified the final results. Although I'm very excited to see more fundamental physics problems being solved with AI.👀23h
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