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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.
HydroTFT is presented, an attention-based rainfall–runoff model based on the Temporal Fusion Transformer that performs multi-horizon streamflow prediction using only antecedent meteorological forcings, static catchment attributes, and hydrologically informed engineered features and provides an accurate, interpretable,...
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...
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...
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