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Agentic retrieval gains 8.7 nDCG points but takes 107.4 seconds per query

huggingface.co by Reza Esfandiarpoor et al. Editor score 8/10

A ReAct retrieval pipeline improves nDCG@10 by 8.7 points over standard retrieval with the same embedding model, but averages 107.4 seconds and 764.1K input tokens per query. The measured quality-cost tradeoff is useful when deciding whether agentic search fits a production retrieval path.

What: Agentic retrieval combines LLM reasoning with retrievers in a ReAct loop to handle complex search tasks.
Method: It improved nDCG@10 by 8.7 points and performed competitively on ViDoRe v3 and BRIGHT, but took 107.4 seconds per query versus 0.67 seconds for standard retrieval.
Why it matters: Its quality and generalization gains come with substantial latency and token costs, a key tradeoff for large-scale deployment.

The ReAct loop combines retrievers' corpus exploration with LLM reasoning for complex retrieval. Sketched from the article's own text.

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