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NVIDIA's NeMo Retriever team has developed an agentic retrieval pipeline that achieved #1 on the ViDoRe v3 pipeline leaderboard and #2 on the BRIGHT reasoning benchmark. Unlike specialized retrieval systems, the pipeline uses a ReACT-based agentic loop where an LLM iteratively searches, evaluates, and refines queries using think, retrieve, and final_results tools. A key engineering decision replaced an MCP server with a thread-safe in-process singleton retriever, eliminating network overhead and deployment complexity. Ablation studies show that pairing frontier models like Claude Opus 4.5 with specialized embedding models (nemotron-colembed-vl-8b-v2 for visual docs, llama-embed-nemotron-reasoning-3b for reasoning tasks) yields the best results. The main tradeoff is cost and latency—averaging 136 seconds per query—but NVIDIA plans to distill these agentic patterns into smaller open-weight models to reduce overhead.

Beyond Semantic Similarity: Introducing NVIDIA NeMo Retriever’s Generalizable Agentic Retrieval Pipeline

The Motivation: Why Semantic Similarity Isn't Enough

Generalization vs. Specialization Across Benchmarks