NVIDIA Study: Agentic Retrieval Boosts nDCG@10 by 8.7 Points but Takes 160x Longer
_reachsumit · x · 2026-10-06
NVIDIA researchers (Reza Esfandiarpoor et al.) present a systematic evaluation of agentic retrieval—LLM agents looping over dense retrievers in a ReAct loop—for complex retrieval tasks.
Key findings:
- Effectiveness: With the same embedding model, agentic retrieval improves nDCG@10 by 8.7 points over standard dense retrieval, which relies on surface-level semantic similarity.
- Generalization: Unlike specialized retrieval methods that struggle out-of-domain, the same pipeline achieves competitive results on both the ViDoRe v3 and BRIGHT leaderboards.
- The cost: On average, a query takes 107.4 seconds vs. 0.67 seconds for standard retrieval, consuming 764.1K input and 5.8K output tokens per query.
The study demonstrates agentic retrieval works but motivates future work on more cost-efficient retrieval agents for large-scale deployment.
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