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AllSpark Research unveils Iris, an open-weight web search agent

AllSpark Research has introduced Iris, an open-weight web search agent system designed to tackle complex, multi-hop questions that often stump current language models. Iris utilizes two models, Iris-mini and Iris-pro, post-trained from the Qwen3.5/3.6 MoE series, and achieves state-of-the-art results on benchmarks like BrowseComp and Humanity's Last Exam. The system's innovation lies in its training methodology, SFT-RL climbing, which alternates between supervised fine-tuning and reinforcement learning, and its focus on an execution harness to manage context saturation during long-horizon searches. AI

IMPACT Iris's novel training approach and performance on complex search tasks could advance the capabilities of open-weight agents for intricate information retrieval.

RANK_REASON The cluster describes the release of a new open-weight agent system, Iris, with novel training methods and benchmark performance. [lever_c_demoted from frontier_release: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AllSpark Research unveils Iris, an open-weight web search agent

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Significant
The cluster describes the release of a new open-weight agent system, Iris, with novel training methods and benchmark performance. [lever_c_demoted from frontier_release: ic=1 ai=1.0]
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model release, product
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Prabhakar Chaudhary ·

    Iris: How SFT-RL Climbing Trains Open-Weight Agents to Search the Web Like a Researcher

    <h1> Iris: How SFT-RL Climbing Trains Open-Weight Agents to Search the Web Like a Researcher </h1> <p>Web search sounds simple until you try to automate it for hard questions. Ask a language model to find the founding date of a company, and it will usually succeed. Ask it to trac…