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A learned shortlist can make private dense retrieval both accurate and practical, achieving near-full-corpus quality with a fraction of the computational cost.
Full-context interventions in Decision Transformers can dramatically shift predictions, but the effectiveness is highly dataset-dependent, revealing potential pitfalls in RTG conditioning.
CVLNet reveals that urban perception mapping can be achieved at scale without relying on limited street view imagery, significantly enhancing our understanding of urban environmental inequality.
