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Shifts in observation and action spaces can drastically alter task success rates by over 30%, revealing critical vulnerabilities in current AI agents' performance on UI tasks.
Open-source QUEST agents, trained solely on 8K synthetic tasks, rival or surpass proprietary research agents, proving that scaling data synthesis can unlock frontier performance.
PINNs can now jointly infer parameters and detect regime-switching in dynamical systems, outperforming traditional methods that treat these tasks separately.
