AI

Beyond Semantic Similarity: Performance and Costs of Agentic Retrieval for Complex Tasks

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Briefings

This study investigates agentic retrieval combining LLM reasoning with retrievers in a ReAct loop. Results show agentic retrieval improves nDCG@10 by 8.7 points on standard tasks and generalizes to ViDoRe v3 and BRIGHT leaderboards, though it costs 107.4 seconds per query versus 0.67 seconds for standard retrieval.

Key points

• Agentic retrieval using ReAct loops with LLMs improved nDCG@10 by 8.7 points compared to standard dense retrieval using the same embedding model.
• The same agentic pipeline achieved competitive results on both the ViDoRe v3 and BRIGHT leaderboards, demonstrating generalizability beyond specific task domains.

Context

Modern information systems rely on dense retrieval for unstructured data, but surface-level similarity often fails for complex tasks. This study introduces agentic retrieval as a solution to bridge this gap.

Limitations

Agentic retrieval significantly increases query time to 107.4 seconds compared to 0.67 seconds for standard retrieval, and token consumption is substantially higher at 764.1K input and 5.8K output tokens per query.

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