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    EurekaBench introduced to assess AI agents’ scientific insight across six domains

    A project contributor says the benchmark examines whether agents can discover new insights through experimentation, beyond trial-and-error optimization.

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    TLDR

    A project contributor says they worked with domain experts to develop EurekaBench, a benchmark spanning six science domains. It is designed to evaluate whether AI agents can discover genuinely new scientific insights. The contributor contrasts that goal with agents’ ability to find solutions through repeated trial and error.

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    5 Sources, first seen 7h ago

    Combined views

    12.8K

    5 Sources, first seen 7h ago

    204 likes
    7h ago
    first seen 7h ago
    204 likes
    10 comments
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    54 reposts

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    10 comments
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    5 Sources

    @JiayiiGengAI scientist agents are great at optimizing and finding solutions through endless trial and error. But is that really what science is about? As Terrance Tao eloquently put, the role of math and science should be more than that – they are "lighthouses" that guide and inspire exploration through understanding and insights. Can AI agents discover novel insights through experimentation? To study these gaps, we worked with domain experts to introduce EurekaBench, a benchmark spanning 6 science domains that evaluates an agent’s ability to discover genuinely new scientific insights. (1/n)7h
    @__howardchenAI agents now seem to solve the hardest problems in math and science, but they haven't yet deepened our understanding at the same rate. They are great problem-solving machines when the objective and optimization process are clear, but discover insights is a lot fuzzier! We build EurekaBench to measure this gap using 6 natural science domains (neuroscience, geophysics, plasma physics, astrophysics, computer science, and chemistry). We test if AI agents can complete the whole scientific discovery loop from understanding the observations, explaining the data by describing the mechanism (e.g., equations), and eventually discovering insights. Results show that the agents don't advance on both improving accuracy and finding insights at the same pace! A lot more details in the thread. Fantastic work led by @JiayiiGeng.6h
    @scott_lindermanRT @__howardchen: AI agents now seem to solve the hardest problems in math and science, but they haven't yet deepened our understanding at…6h
    @MayeeChenRT @__howardchen: AI agents now seem to solve the hardest problems in math and science, but they haven't yet deepened our understanding at…6h

    5 Sources

    @JiayiiGengAI scientist agents are great at optimizing and finding solutions through endless trial and error. But is that really what science is about? As Terrance Tao eloquently put, the role of math and science should be more than that – they are "lighthouses" that guide and inspire exploration through understanding and insights. Can AI agents discover novel insights through experimentation? To study these gaps, we worked with domain experts to introduce EurekaBench, a benchmark spanning 6 science domains that evaluates an agent’s ability to discover genuinely new scientific insights. (1/n)7h
    @__howardchenAI agents now seem to solve the hardest problems in math and science, but they haven't yet deepened our understanding at the same rate. They are great problem-solving machines when the objective and optimization process are clear, but discover insights is a lot fuzzier! We build EurekaBench to measure this gap using 6 natural science domains (neuroscience, geophysics, plasma physics, astrophysics, computer science, and chemistry). We test if AI agents can complete the whole scientific discovery loop from understanding the observations, explaining the data by describing the mechanism (e.g., equations), and eventually discovering insights. Results show that the agents don't advance on both improving accuracy and finding insights at the same pace! A lot more details in the thread. Fantastic work led by @JiayiiGeng.6h
    @scott_lindermanRT @__howardchen: AI agents now seem to solve the hardest problems in math and science, but they haven't yet deepened our understanding at…6h
    @MayeeChenRT @__howardchen: AI agents now seem to solve the hardest problems in math and science, but they haven't yet deepened our understanding at…6h