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KVLearn, a learning-based retention framework that makes keep/evict decisions as first-class cost-optimization choices in disaggregated LLM serving is presented and evaluated on both text and multimodal workloads.
This work introduces RUBRIC, a generator-agnostic filtering framework that formulates synthetic sample selection as a quality-over-quantity optimization problem and shows that RUBRIC improves F1-macro and recall while maintaining comparable ROC-AUC across several generators.
This article provides a unified view of context-adaptive inference across three traditions that are usually treated separately: explicit adaptation in statistics, rapid task-specific adaptation in meta-learning and transfer, and implicit adaptation in large foundation models via prompting, retrieval, and expert routing...
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