Beyond Relevance-Centric Retrieval: Rubric-Oriented Document Set Selection and Ranking
As large language models and AI agents become the primary consumers of search results, document set quality determines the upper bound of downstream generation. Yet existing evaluation systems remain confined to scoring documents independently and aggregating via nDCG, ignoring inter-document interactions (redundancy, conflict, complementarity) and unable to answer what makes one document set better than another. To address these issues, we propose a complete evaluate-diagnose-optimize framework
Record details
Published: 21 July 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Healthcare · Agents & autonomy
Retrieved: 23 July 2026
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How to cite this record
ethics.ai (21 July 2026), “Beyond Relevance-Centric Retrieval: Rubric-Oriented Document Set Selection and Ranking,” evidence record 12653, https://ethics.ai/record/12653 (originally published by HuggingFace Daily Papers).
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