XYZAILab/XYZ-Aquila-mini · Hugging Face

Source: https://huggingface.co/XYZAILab/XYZ-Aquila-mini

XYZ-Aquila-mini

An open-weight thinking model for Deep Search.

https://huggingface.co/XYZAILab/XYZ-Aquila-mini#introductionIntroduction

XYZ-Aquilais a family of open-weight Deep Search agents developed byXYZ AI Lab. XYZ-Aquila-mini is post-trained fromQwen3.6-35B-A3Bthrough a bounded-explorationAI4AIpipeline: humans define the target capability, development evidence, constraints, risk boundaries, and acceptance policy, while AI agents diagnose failures and propose scoped interventions across data, post-training, runtime, context management, tools, evaluation, and infrastructure.

The released checkpoint is athinking modelwith Qwen-compatible reasoning and tool-call formats. It is optimized for agentic search, including long-horizon planning, English and Chinese web browsing, multi-source evidence aggregation, source verification, and recovery from failed environment interactions. The open-sourceAxisAgentic harnessprovides the concretesearch/scrape/pythontool implementations, fixed tool contract, replayable context management, and benchmark evaluation workflow; these capabilities are supplied by the surrounding harness rather than by the checkpoint alone.

https://huggingface.co/XYZAILab/XYZ-Aquila-mini#benchmark-resultsBenchmark Results

The external benchmark suite was held out from routine AI4AI optimization. Following the technical report, evaluation uses a ReAct-style search harness with web search, webpage extraction, stateful Python execution, and a maximum 256K context. XYZ-Aquila-mini obtains the highest reported score in every column of the evaluated sub-40B open-weight comparison. XYZ-Aquila-pro obtains the highest reported score in every column of the evaluated sub-400B open-weight comparison.

The figure provides a visual overview across six agentic search benchmarks. The tables below transpose the comparison: each row is a benchmark and each column is a model within the group.

https://huggingface.co/XYZAILab/XYZ-Aquila-mini#small-scale-open-weight-40bSmall-scale open-weight (<40B)

Open-weight systems with fewer than 40B parameters, including XYZ-Aquila-mini.

BenchmarkXYZ-Aquila-miniAgents-A1Nex-N2-miniapodex-miniMiroThinker 1.7 miniBrowseComp78.875.574.171.567.9BrowseComp-ZH82.9--79.6†80.6--DeepSearchQA89.5--87.2†82.2--GAIA97.196.0----80.3LiveBrowseComp48.729.6†41.4†32.8†34.9†HLE51.147.637.1†46.836.4WideSearch80.8--62.0--73.3†

https://huggingface.co/XYZAILab/XYZ-Aquila-mini#large-scale-open-weight-400bLarge-scale open-weight (<400B)

Open-weight systems with fewer than 400B parameters, including XYZ-Aquila-pro.

BenchmarkXYZ-Aquila-proNex-N2-ProMiroThinker 1.7apodex-1.0BrowseComp84.883.7†74.075.5BrowseComp-ZH85.179.6†75.382.6DeepSearchQA92.592.3†--84.6LiveBrowseComp53.750.4†34.1†--HLE53.350.0†42.949.0WideSearch81.275.6----

https://huggingface.co/XYZAILab/XYZ-Aquila-mini#xyz-aquila-pro-vs-larger-scale-and-closed-source-modelsXYZ-Aquila-pro vs. Larger-Scale and Closed-Source Models

XYZ-Aquila-pro compared with larger-scale open-weight and closed-source models.

BenchmarkXYZ-Aquila-proapodex-h1DeepSeek-V4- Pro-MaxKimi-K2.6Claude Opus 4.7GPT-5.5 xhighBrowseComp84.890.383.483.279.384.4BrowseComp-ZH85.184.1--------DeepSearchQA92.594.4--92.589.1--LiveBrowseComp53.7--38.331.7----HLE53.360.8--55.554.752.2WideSearch81.2----80.8---- All values are percentages. HLE denotes Humanity’s Last Exam. DeepSearchQA uses F1, WideSearch uses Item F1 Max@4, and the remaining benchmarks use accuracy. Rows with no reported result across an entire group are omitted;\-\-indicates an unreported result within an otherwise populated row.†marks results reproduced under the common evaluation setup; other baseline values come from public reports or benchmark submissions. See thetechnical reportfor full provenance and analysis.

https://huggingface.co/XYZAILab/XYZ-Aquila-mini#quickstartQuickstart

https://huggingface.co/XYZAILab/XYZ-Aquila-mini#sglang-deploymentSGLang Deployment

Use a recent SGLang release (sglang\>=0\.5\.10). The example below launches an OpenAI-compatible endpoint with Qwen reasoning and tool-call parsers. It uses tensor parallelism across eight GPUs; reduce the context length if the deployment does not have enough memory.

uv pip install "sglang[all]>=0.5.10"

MODEL_PATH=XYZAILab/XYZ-Aquila-mini
SERVED_MODEL=XYZ-Aquila-mini

python -m sglang.launch_server \
  --model-path "${MODEL_PATH}" \
  --served-model-name "${SERVED_MODEL}" \
  --port 8000 \
  --tp-size 8 \
  --mem-fraction-static 0.8 \
  --context-length 262144 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder

ForXYZ\-Aquila\-pro, replaceMODEL\_PATHandSERVED\_MODELwith the pro repository and use the tensor-parallel configuration appropriate for your hardware.

https://huggingface.co/XYZAILab/XYZ-Aquila-mini#recommended-sampling-configRecommended Sampling Config

These are recommended starting values for thinking-mode agentic search. Tune them for the target task and harness.

temperature: 1.0
top_p: 0.95
repetition_penalty: 1.05
chat_template_kwargs:
  enable_thinking: true
  preserve_thinking: true

Keep the input and generated response within the 262,144-token context window.

https://huggingface.co/XYZAILab/XYZ-Aquila-mini#citationCitation

@techreport{xyz_aquila_2026,
  title       = {AI4AI at Scale: A Full-Pipeline System for Enhancing LLM Agentic Capabilities},
  author      = {{XYZ Agentic Team}},
  institution = {XYZ AI Lab},
  year        = {2026},
  url         = {https://xyz-lab.ai/blogs/ai4ai-at-scale/assets/bounded-exploration-ai4ai-system-optimization.pdf}
}