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This work proposes an algorithm that not only tackles the issue of sample average of performance estimates generated by the optimization algorithm, but also provides a consistent online estimator for the variance of the performance.
The analysis from independent random search to a broader family of local search operators, and it is proved that when sensitivities vary across instances, no adaptive policy can avoid worst-case regret that grows linearly in the trajectory length.
In Learning to Simulate from Heavy-Tailed Distribution via Diffusion Model, the authors pinpoint why standard diffusion models break down on heavy-tailed targets and propose a Student-t-based heavy-tailed diffusion (SHD) framework that fixes both ends of the pipeline.
Spec2Twin-Chain is considered, a framework that formulates blockchain digital-twin construction as a bi-level optimization problem and can construct behaviorally accurate twins, improve initial designs through iterative feedback, and reuse calibrated twins to support downstream decisions.
3D-DefectBench is introduced, a benchmark and framework for systematic analysis of VLM-based 3D defect detection pipelines, and finds that automated judges should be evaluated as complete pipelines and calibrated across human reference regimes, rather than benchmarked only as standalone models.
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