Az NVIDIA új kereső- és dokumentumfeldolgozó AI-modelleket adott ki

An agent’s answers depend on the evidence it can retrieve. When that evidence is incomplete or irrelevant, the agent will repeat searches and inspect additional documents, consuming more inference tokens before completing the task. Better retrieval can help agents reach grounded answers with fewer wasted tokens. At enterprise scale, that waste leads to massive inefficiencies. Agents need the right retrieval models and systems to reach insights faster. Today, we are expanding the Nemotron Retriever collection with new open, commercially available models for embedding, reranking, and document extraction. The new models include Nemotron 3.5 Embed 8B BF16, 1B BF16, 1B NVFP4, Nemotron 3.5 Rerank 8B BF16, and Nemotron 3.5 Parse at 631M parameters. Together, these state-of-the-art models bring multimodal retrieval, relevance ranking, and document processing to enterprise agent workflows. Retrieval quality affects both the answers an agent produces and the LLM inference cost. Relevant evidence can help the agent answer a question or better decide its next step, and reranking improves the quality of evidence that enters its context. Consider an agent asked to identify products at risk of stockouts next quarter and explain why. It must combine demand forecasts in charts, inventory levels in tables, and supplier lead times in reports, then reason about whether replenishment will arrive before inventory runs out. Completing the task requires finding the relevant evidence across documents and using it together in the analysis. To understand how retrieval affects the agent’s ability to combine evidence and complete a task, we evaluated Nemotron 3.5 Embed and Rerank across ViDoRe v3, BRIGHT, and BrowseComp-Plus. ViDoRe v3 uses combined text and image inputs, while BRIGHT and BrowseComp-Plus use text-only inputs. In this pipeline, an agent queries the corpus using a search tool, inspects the evidence, and refines its next search attempt to find the most relevant information to a given query. The experiments below vary how the search tool is implemented: embedding only, embedding plus reranking, and with different models for each. The LLM powering the agent, tools, task set, corpus, and execution policy remain fixed while the retrieval configuration changes. We measure the agent's ability to find the relevant information for each task and total agent inference tokens per task, including input and output tokens across all turns. Figure 1. End-to-end task success versus total agent inference tokens per task across retrieval configurations. Behind these agent successes are Nemotron 3.5 Embed and Nemotron 3.5 Rerank, which retrieve evidence across text and images and prioritize it for the agent’s context. Nemotron 3.5 Embed builds on the previous generation’s Nemotron 3 Embed text embedding models to retrieve evidence from document images, including tables, charts, infographics, datasheets, and schematics. This allows applications to search information conveyed by visual content alongside document text. The four models provide different options for retrieval and second-stage ranking: Table 1. Nemotron 3.5 Embed and Nemotron 3.5 Rerank model capabilities and use cases across various sizes and variants. On ViDoRe v3.1, Nemotron 3.5 Embed 8B BF16 achieves 61.17 nDCG@10, ranking first among dense retrieval models. This is an 8.94-point improvement over the 52.23 score achieved by the Llama Nemotron Embed VL 1B model. The 1B model brings most of this visual retrieval quality into a smaller model. Distilled from the 8B teacher, Nemotron 3.5 Embed 1B BF16 scores 58.38 nDCG@10, retaining 95.4% of the teacher’s score. Figure 2. Nemotron 3.5 Embed 8B BF16 ranked first among dense retrieval models on the ViDoRe v3.1 leaderboard as of 10/20/2026. We also compare the models with Qwen3-VL-Embedding, EmbeddingGemma-2, and the previous-generation NVIDIA model across ViDoRe v3, Korean ViDoRe and MIRACL Vision. The chart below reports retrieval accuracy