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Georgia Institute of Technology
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Semilinear SPDEs driven by heavy-tailed, discontinuous L茅vy flights can finally be treated purely pathwise without sacrificing the infinite-dimensional Markov property.
Frontier coding agents can now write 100% functionally correct GPU physics simulations, yet they match human-expert performance on just 22% of tasks鈥攔evealing a stark capability gap between generating working code and producing performant systems.
Neural PDE solvers no longer need retraining for new boundary conditions: learning domain geometry through its harmonic measure enables zero-shot Poisson solutions via simple re-integration against a single transformer kernel.
Today's best AI agents can only solve 55% of real-world academic tasks that university students find challenging, revealing a significant gap between current AI capabilities and the demands of academic workflows.
