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  Google Research releases GIST: feed into AI models more efficiently for tasks like AI Overviews and RAG-style retrieval

  Posted by WebLinkr on January 30, 2026 at 9:36 pm

  

  At NeurIPS 2025, we introduced Greedy Independent Set Thresholding (GIST), a novel algorithm that helps solve this issue by balancing data “diversity” (ensuring the selected data is not redundant) and data “utility“ (data that is relevant and useful for the task). GIST not only outperforms state-of-the-art benchmarks tasks, such as image classification, but it does so with a mathematical guarantee about its solution quality.

  The conflict: Why smart sampling is hard

  When selecting a subset of data, researchers must balance two often conflicting objectives: diversity and utility. Enforcing data diversity ensures the selected points aren’t redundant. Utility measures the overall usefulness or informational value of the selected subset.

  WebLinkr replied 4 hours, 35 minutes ago 1 Member · 0 Replies
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