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LRanker: Scaling LLM Ranking to Millions of Candidates with Test-Time Scaling
Large language models (LLMs) offer strong semantic representations for ranking, but processing candidate texts within limited contexts and repeatedly invoking an LLM for candidate-level comparisons can be costly when candidate pools contain millions of items. We propose LRanker, a framework for scal
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