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Training-stage ablations further show that financial process supervision and execution-feedback reinforcement learning yield successive improvements in both answer accuracy and GPT-5.6-judged grounded accuracy.
DeepSTE is proposed, a deep spectral temporal embedding framework for dynamic graphs that learns RFF representations via Monte Carlo importance sampling with a tractable proposal distribution and adopts a data-dependent scale parameter to construct the frequency proposal distribution reflecting time–frequency uncertain...
Experiments show that LAUA substantially mitigates performance degradation under modality missingness across retrieval and regression tasks, attaining up to 20% relative improvement in MRR for retrieval and up to 24.3% relative improvement in MSE for regression.
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