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PRODiGI, a pretrained data-to-program model that infers an explicit, executable generative program in a single forward pass, is introduced and program-space fine-tuning is introduced, which refines differentiable program parameters by matching generated and empirical samples while keeping model parameters intact.
This first study of depth-adaptive early-exit for pretrained outlier detection models finds that exiting at the optimal intermediate layer can also improve detection performance on diverse real-world benchmarks by 4.7-7.3% on average, consistent across three distinct foundation models.
This work introduces TANGCO (Topology-Aware Neural Graph-Guided Capacity Optimization), which uses a graph neural network policy trained through the cascade simulator with policy-gradient learning and a heuristic anchor to allocate a fixed capacity budget across nodes to resist cascades under local load redistribution.
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