Iris-pro scores 92.9 on DeepSearchQA with a single ReAct agent
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Two search agents, Iris-mini and Iris-pro, were trained at massive scales (35B-A3B and 397B-A17B) and achieved state-of-the-art results among open-source search agents, reaching scores of up to 92.9 on certain benchmarks, which enables shipping more accurate and capable search agents in production environments.
Iris-pro, a 397B parameter search agent, achieves 92.9 accuracy on DeepSearchQA with context management enabled, setting a new benchmark for open-source search agents. This pushes the frontier of multi-hop reasoning and retrieval performance, enabling production teams to deploy more reliable and efficient agents for complex search tasks without requiring proprietary systems or costly test-time verification.