Iris README

The project's README from github.com/AllSpark-Research/Iris. As one markdown file: README.md.

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Iris

Climbing to the Search Frontier.

Iris-mini (35B-A3B) and Iris-pro (397B-A17B) are open-weight search agents post-trained from the Qwen3.5/3.6 series. A capable search agent has to decide what to search, how to read what comes back, when to keep going, and when the evidence it has gathered is enough. Iris is trained for exactly that loop.

Performance

30–35B

Model Size BrowseComp BrowseComp-ZH DeepSearchQA HLE
MiroThinker-1.7-mini 30B 67.9 72.3 – 36.4
FORT-Searcher 30B 72.2 75.0 – –
Apodex-1.0-mini 35B 71.5 80.6 82.2 46.8
Nex-N2-mini 35B 74.1 79.6r 87.2r 37.1r
Agents-A1 35B 75.5 – – 47.6
XYZ-Aquila-mini 35B 78.8 82.9 89.5 51.1
Iris-mini 35B 82.2 84.8 86.9 52.3

~400B

Model Size BrowseComp BrowseComp-ZH DeepSearchQA HLE
MiroThinker-1.7 397B 74.0 75.3 – 42.9
Apodex-1.0 397B 75.5 82.6 84.6 49.0
Nex-N2-Pro 397B 83.7 79.6r 92.3r 50.0r
XYZ-Aquila-pro 397B 84.8 85.1 92.5 53.3
Iris-pro 397B 88.6 85.1 92.9 56.4

DeepSearchQA is scored with F1, the rest with accuracy; HLE uses the text-only subset. Iris numbers use the discard-all context-management setting; baselines come from their public reports, each under its own context management. r reproduced by the XYZ-Aquila team.

Models

Model Base Params (total / active) Context Download
Iris-mini Qwen3.6-35B-A3B 35B / 3B 256K 🤗 Iris-mini
Iris-pro Qwen3.5-397B-A17B 397B / 17B 256K 🤗 Iris-pro

Context Management

Long-horizon search runs out of context before a hard question is resolved, so every serious system carries some mechanism for this. It is worth enough that a single published number belongs to the agent and its harness together, which is why we report every benchmark in both regimes, under one tool set, one context limit, and one judge.

Setting BrowseComp BrowseComp-ZH DeepSearchQA HLE
Iris-mini
w/o 64.7 72.3 81.0 43.2
retry – 83.0 89.1 52.0
discard-all 82.2 84.8 86.9 52.3
discard-all + retry 85.9 85.1 89.9 52.4
Iris-pro
w/o 72.6 76.8 86.4 50.8
retry – 84.1 92.3 56.6
discard-all 88.6 85.1 92.9 56.4
discard-all + retry 90.3 85.1 93.4 56.6

discard-all clears the accumulated tool history and restarts from the question once the running context crosses a threshold. retry restarts an episode that ended without a parseable answer, carrying forward a short summary of what was already ruled out. We report discard-all as the headline setting even where adding retry scores higher.

Evaluation

Iris-Harness/ is the harness behind every number above: the agent loop, the two tools, the context-management strategies, the four benchmarks and the graders. It runs against any OpenAI-compatible endpoint.

cd Iris-Harness && uv sync
uv run python data/prepare_data.py
bash scripts/run_eval.sh --base-url http://127.0.0.1:21234/v1 --llm-config iris-mini \
  --benchmarks "browsecomp:0:1" --context-discard-threshold 131072

Acknowledgements

Iris is built on open-source work, and we are grateful to the teams behind it:

Citation

If you find Iris useful in your research, please cite our paper:

@misc{liu2026irisclimbingsearchfrontier,
  title={Iris: Climbing to the Search Frontier},
  author={Ziyuan Liu and Hengqi Liu and Zichuan Wang and Yang Qin and Jiachen Liang and Xu Chu and Shaowei Chen and Yuantao Gu and Zhaokai Luo and Yao Hu and Mu Chuan},
  year={2026},
  eprint={2609.04304},
  archivePrefix={arXiv},
  primaryClass={cs.AI},
  url={https://arxiv.org/abs/2609.04304},
}

The data construction and training pipelines are coming soon.

Questions or collaboration: reach us at [email protected].

Iris (AllSpark-Research/Iris)

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101 ·GitHub
Iris
这是什么？Iris 是一对开放权重的搜索智能体模型（Iris-mini，35B-A3B，和 Iris-pro，397B-A17B），在 Qwen3.5/3.6 系列的基础上后训练而来，用来决定搜什么、怎么读搜索结果、什么时候继续搜、以及什么时候证据已经足够。
这是训练好的模型还是一个框架？两者都是：Iris 是一对公开发布的模型权重，同时仓库还附带了 Iris-Harness，也就是用于运行和评估这些模型的智能体循环、工具和上下文管理代码。
我该怎么把它跑起来？从 Hugging Face 下载 Iris-mini 或 Iris-pro 的权重，然后通过 Iris-Harness 运行，它可以对接任何兼容 OpenAI 的接口。
使用 Iris 需要特定的基础模型吗？Iris 分别在 Qwen3.6-35B-A3B（对应 Iris-mini）和 Qwen3.5-397B-A17B（对应 Iris-pro）的基础上后训练而来，因此继承了这两个基础架构。
我能复现 Iris 的训练过程吗？目前还不能——只发布了模型权重和评估框架，数据构建和训练流程尚未开源。
'discard-all' 这个上下文管理设置是什么？当运行中的上下文超过设定阈值时，它会清空积累的工具调用历史，让智能体从最初的问题重新开始。这是 Iris 报告结果时使用的主打设置。
Iris 在哪些基准上做了评估？BrowseComp、BrowseComp-ZH、DeepSearchQA 和 HLE（Humanity's Last Exam，纯文本子集），其中 DeepSearchQA 用 F1 打分，其余用准确率打分。
Iris-mini 和 Iris-pro 有什么区别？Iris-mini 是一个 35B 参数、3B 激活的模型（基于 Qwen3.6），而 Iris-pro 是一个大得多的 397B 参数、17B 激活的模型（基于 Qwen3.5）；两者都支持 256K 上下文窗口，并且 Iris-pro 在所有基准上的得分都更高。
Iris 建立在哪些开源项目之上？作者将 MiroThinker、Relax、ms-swift 和 slime 列为 Iris 所依赖的开源基础。
What are similar projects?RSIGame, RSI-Anything, Kimi-K3, MSA, NeoHorse, jeeves, DeepAnalyze, ZDTaichu5.0-9B
Which topics is it in?context-window, deep-research, mixture-of-experts, reasoning, llm-agents, autonomous-agents, search, qwen, evaluation, academic-research, large-language-models, agentic-ai
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AllSpark-Research
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CommitSep 28
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Commits · 90 Days