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Multi-agent systems are increasingly deployed for complex knowledge work, yet their orchestration layers remain largely memoryless: each new task is decomposed, assigned, and executed from scratch with no benefit from prior successful executions. We present WorkflowOps, a multi-agent workflow orchestration framework th...
OOPMAS is introduced, a training-free framework that generates both the agent set and the coordination workflow at the granularity of individual queries that achieves 89.6% accuracy on a mixed-task benchmark of queries spanning code, math, and QA.
Evaluating ecological time series is critical for benchmarking model performance in many important applications, including predicting greenhouse gas fluxes, tracking soil moisture and soil–atmosphere interactions, and monitoring hydrological cycles. Traditional numerical metrics (e.g., R-squared, root mean square error...
An action-conditioned world-modeling framework for Earth-system emulation that reformulates simulator trajectories as supervision for controllable state-transition learning that preserves competitive long-horizon emulation accuracy while enabling controllable structural interventions and coherent responses in coupled e...
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