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    Muse Spark and mathematicians collaborate on six research papers

    Meta says researchers used Muse Spark 1.1 and 1.2 through its standard chat interface, with human review.

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    TLDR

    Meta AI Research released six Muse Spark-assisted papers; the company says five answer previously open questions. Mathematicians guided the research, verified results and reviewed the work, while the papers identify passages drafted mainly by AI or researchers. The work spans six areas of mathematics. Meta also notes that independent teams announced solutions to some of the same problems.

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    4 Sources, first seen 4h ago

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    Meta AI Research published six papers produced through collaborations between mathematicians and Muse Spark. The company says five of the papers present answers to previously open research questions.

    The researchers used Muse Spark 1.1 and 1.2 in Thinking Mode through the regular Meta AI chat interface, without a custom research scaffold. Meta describes the goal as helping researchers develop mathematical insights, rather than producing papers at scale.

    Researchers guided and checked the work

    Meta says mathematicians selected and guided the research, working with Muse Spark to explore ideas and develop arguments. A second group of mathematicians reviewed the work. Each paper identifies passages drafted primarily by researchers and passages drafted primarily by AI, according to the announcement.

    The reported division of work varied by project. For a group-theory paper, Muse Spark generated a GAP search program that found a counterexample, which researchers verified and developed into an argument. In the arithmetic-physics project, Meta says the model helped connect ideas from number theory and p-adic string theory, generated candidate proofs and drafted three technical sections that researchers checked, corrected and refined.

    Six areas of mathematics

    The papers cover probability, differential equations, group theory, optimization, arithmetic physics and non-associative algebra. Their topics include a threshold for fitting Gaussian points to an ellipsoid, wave collapse, counterexamples to algebraic conjectures and a connection between two-point functions and height functions on curves.

    Meta says it learned after completing the work that outside teams had independently announced solutions to some of the same problems using different approaches. The company says those contributions and their relationship to its papers are acknowledged in the papers.

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    Today's Rank

    #12

    Today's Rank

    #12

    5 Sources

    Meta AI ResearchSolving Open Research Problems Together23h
    @shuchaobiEarlier this year, our focus shifted from Olympiad-level problems to tackling open research questions. Having majored in mathematics, I realized that I lacked the natural genius required to solve the specific open problems I was passionate about. That realization led me to launch my career in applied machine learning. Since last year, however, my objective has evolved: rather than trying to solve these open problems entirely on my own, I am now dedicated to training AI models to solve them. For example, in collaboration with a few graduate school friends who are now math professors, we have already made meaningful progress on an open problem in arithmetic physics. Industry-wide progress along this trajectory has been nothing short of phenomenal. Mathematics resembles a vastly expanded game of Go—it functions within a structured environment and possesses intrinsic self-verification. We experienced mathematics' "AlphaGo moment" this year, marking a point of no return. We anticipate that a similar intelligence transition will unfold across other scientific disciplines, albeit at a different pace owing to physical verification constraints in biology (to be verified in the human body), chemistry, and physics (to be verified in the universe). As this intelligence revolution unfolds, our primary mission is to empower everyone to navigate and thrive through this smooth transition. The models used to tackle these open research questions were built on earlier iterations of Muse Spark; our current and upcoming models are already significantly more capable. We will keep you posted. https://research.meta.ai/blog/solving-open-research-problems-together4h
    @TrapitBansalWe are still learning how AI and researchers should collaborate to produce new results. Here's a first set of results from our attempt at this where researchers used earlier versions of Muse Spark to solve open research problems. The models help explore ideas and take care of tedious problem solving; researchers guide, verify, and take ownership of the result. Excited to see this evolve with more feedback from the community, and better models!2h
    @alexandr_wangmathematicians and muse spark collaborated to solve 6 open problems in math: 1. The Strict Threshold for Gaussian Ellipsoid Fitting 2. Finite-Time Blow-Up of Radial Negative-Energy Solutions for the Mass-Critical Biharmonic Nonlinear Schrödinger Equation 3. Semiabelian Groups Need Not Be Monomial 4. Tightness of the Cycle-Based Relaxation for Completed Length-Three Alpha-Cycles 5. String Two-Point Function = Height Function on a Curve 6. On Solvable Evolution Algebras and a Conjecture by García-Martínez and Pérez-Rodríguez1h

    5 Sources

    Meta AI ResearchSolving Open Research Problems Together23h
    @shuchaobiEarlier this year, our focus shifted from Olympiad-level problems to tackling open research questions. Having majored in mathematics, I realized that I lacked the natural genius required to solve the specific open problems I was passionate about. That realization led me to launch my career in applied machine learning. Since last year, however, my objective has evolved: rather than trying to solve these open problems entirely on my own, I am now dedicated to training AI models to solve them. For example, in collaboration with a few graduate school friends who are now math professors, we have already made meaningful progress on an open problem in arithmetic physics. Industry-wide progress along this trajectory has been nothing short of phenomenal. Mathematics resembles a vastly expanded game of Go—it functions within a structured environment and possesses intrinsic self-verification. We experienced mathematics' "AlphaGo moment" this year, marking a point of no return. We anticipate that a similar intelligence transition will unfold across other scientific disciplines, albeit at a different pace owing to physical verification constraints in biology (to be verified in the human body), chemistry, and physics (to be verified in the universe). As this intelligence revolution unfolds, our primary mission is to empower everyone to navigate and thrive through this smooth transition. The models used to tackle these open research questions were built on earlier iterations of Muse Spark; our current and upcoming models are already significantly more capable. We will keep you posted. https://research.meta.ai/blog/solving-open-research-problems-together4h
    @TrapitBansalWe are still learning how AI and researchers should collaborate to produce new results. Here's a first set of results from our attempt at this where researchers used earlier versions of Muse Spark to solve open research problems. The models help explore ideas and take care of tedious problem solving; researchers guide, verify, and take ownership of the result. Excited to see this evolve with more feedback from the community, and better models!2h
    @alexandr_wangmathematicians and muse spark collaborated to solve 6 open problems in math: 1. The Strict Threshold for Gaussian Ellipsoid Fitting 2. Finite-Time Blow-Up of Radial Negative-Energy Solutions for the Mass-Critical Biharmonic Nonlinear Schrödinger Equation 3. Semiabelian Groups Need Not Be Monomial 4. Tightness of the Cycle-Based Relaxation for Completed Length-Three Alpha-Cycles 5. String Two-Point Function = Height Function on a Curve 6. On Solvable Evolution Algebras and a Conjecture by García-Martínez and Pérez-Rodríguez1h