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HB-PVI is a hierarchical Bayesian framework that optimizes personalization decisions in complex activity recognition by balancing gains and costs, demonstrating that a population-first deployment policy can reduce labeling expenses while maintaining performance.
This paper introduces Opal (Opportunity-aware Policy Authorization for Laboratories), a framework that certifies whether adaptive experimentation should be enabled by precommitting to non-trivial adaptation, controlled target risk, and positive executed value after cost. It establishes an impossibility boundary and demonstrates the method on a Cell Painting dataset, achieving risk control and positive value.
Proposes an uncertainty-gated router that doubles the selected key blocks for queries with uncertain cutoff margins, improving recall and accuracy in block-sparse attention for long-context language models, validated on multiple architectures.
This paper introduces a two-stage inference-time budget control method for LLM search agents, using Value-of-Information scores to optimize tool-call and token allocation during multi-hop question answering.