Iris-mini and Iris-pro are the strongest open-weight search agents in their class

The AllSpark team has released Iris-mini and Iris-pro, two open-source search agents along with a full training recipe. According to the paper, the training data and models also improved performance on tasks they were never trained for, including general tool use and office work.

Search agents built on language models research the web on their own. They need to understand the question, decide what to search for, interpret the results, and judge when they’ve gathered enough evidence for an answer. How much of that the models actually do is debated. On established benchmarks, leading AI systems of this kind mostly use the web to confirm knowledge they already picked up during training. In a new paper, Chinese lab AllSpark describes two search agents of different sizes. Iris-mini has 35 billion parameters, and Iris-pro has 397 billion. Both build on Qwen-series models (Qwen3.6-35B-A3B and Qwen3.5-397B-A17B), work with a 256,000-token context window, and deliver the strongest results among open-weight search agents in their respective size class, according to the team. Training questions are reverse-engineered from the web’s link structure The training pipeline builds tasks backward from the link structure of web pages. Starting from a seed page and its outgoing links, it constructs a graph of terms and relationships. From that graph, it generates a multi-step question whose answer requires chaining several connected steps together. Every term except the final answer gets replaced with a paraphrase, so no clue can be resolved through a simple text search. The agent has to reason, not just look things up. Only questions that a reference model can’t solve without tools but can solve with the right sources make it into the dataset. That keeps the tasks both hard and clearly verifiable. A stronger teacher model generates solution paths made up of reasoning, search queries, and results.