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An author-facing LLM system that generates a broad pool of atomic concerns and compresses them into a short report that provides broad coverage with a large candidate pool but compresses poorly is studied.
This work proposes VDA (Visual Distribution Anchoring), a training-free target adaptation framework that augments a frozen semantic classifier with class-level visual prototypes estimated offline from an unlabeled target pool, and shows that class-specific partitioning drives gains and that visually local pseudo-label...
An analytical framework coupling communication limits, a roofline GPU service model, stochastic latency, and utilization-aware cost across three model classes, three offloading strategies, and three communication generations is applied to New York City.
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