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

We are thrilled to announce that NVIDIA NeMo Retriever team has developed a new agentic retrieval pipeline that has officially secured the #1 spot on the ViDoRe v3 pipeline leaderboard. In addition, this exact same pipeline architecture achieved the #2 spot on the highly demanding, reasoning-intensive BRIGHT leaderboard.

In the rapidly evolving landscape of AI retrieval, many solutions are highly specialized, engineered to perform exceptionally well on specific, narrow tasks. However, real-world enterprise applications rarely have the luxury of perfectly curated, single-domain data. They require systems that can seamlessly adapt to a wide variety of challenges—from parsing complex visual layouts to executing deep logical reasoning.

That is why we prioritized generalizability in our design. Rather than relying on dataset-specific heuristics, we built an agentic pipeline that dynamically adapts its search and reasoning strategy to the data at hand. This allows us to deliver state-of-the-art performance across vastly different benchmarks without requiring any underlying architectural changes.

Here is a look at how we built it.

The Motivation: Why Semantic Similarity Isn’t Enough

For years, dense retrieval

 

 

 

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