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Current AI agents excel in structured tasks but falter at generating novel insights and tackling open-ended scientific challenges.
By rethinking hypothesis generation as a sampling problem, this framework boosts both the quality and diversity of scientific hypotheses, challenging the status quo of optimization-focused methods.
The hardest AI tasks remain largely unsolved, with current models achieving only a 2.6% success rate on economically valuable workflows.
