Iris Advances Toward the Search Frontier
agents training
| Source: HF Papers | Original article
Researchers introduce Iris‑mini and Iris‑pro, search agents built on 35B‑A3B and 397B‑A17B scales, using a novel data pipeline that creates multi‑hop tasks from web hyperlink structures.
A research team has unveiled two new search‑oriented language agents, Iris‑mini and Iris‑pro, marking a notable step toward more capable AI‑driven web search. The models were trained at the 35 billion‑parameter (35B‑A3B) and 397 billion‑parameter (397B‑A17B) scales, respectively, using a bespoke data pipeline that constructs tasks from the hyperlink structure of a large web corpus. By authoring multi‑hop chains over an entity graph distilled from a seed page and its out‑links, the authors rewrite every non‑answer entity into a descriptive reference, ensuring that questions cannot be solved by simple string matching. Only queries that a reference model fails to answer in a closed‑book setting are admitted, with the supporting evidence supplied during inference.
In benchmark tests, Iris‑pro displayed “strong intrinsic search capabilities.” Within the standard ReAct framework it matched or outperformed heavyweight competitors such as MiroThinker‑H1 and Apodex‑1.0‑H, despite those models running on substantially larger compute budgets. The results suggest that the new training recipe and task construction method yield measurable gains in search intelligence, even though a performance gap remains when compared with the current frontier of AI search systems.
The development matters because it pushes the envelope on how language models can be harnessed for multi‑step information retrieval, a capability that underpins next‑generation search experiences and research assistants. It also demonstrates that scaling alone is insufficient; the way training data are engineered—here, reverse‑constructed from hyperlink graphs—plays a crucial role.
As we reported on 6 September 2026, OpenAI’s swarm of agents had already begun reaching the open internet. Iris adds a fresh, independently‑scaled approach to that trajectory. The next milestones to watch include broader evaluations on real‑world search workloads, potential integration into consumer products, and whether further scaling or refinements can finally close the gap to the leading frontier models.
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