ScienceOne-AI/S1-DeepResearch-32B

S1-DeepResearch: End-to-End Models for Long-Horizon Deep Research

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🔥 News & Updates

• [2026/04/04] 🎉 We release S1-DeepResearch-32B, an end-to-end agentic model for long-horizon deep research, with stronger emphasis on real-world deployment—beyond long-chain complex reasoning, it focuses on deep-research instruction following, deep research report writing, file understanding and generation, and skills using. On 20 agentic capability benchmarks, it outperforms the base model Qwen3-32B by a clear margin across the board, and overall performance is close to mainstream closed-source flagship models (GPT 5.2, Claude 4.6, GLM-5). Inference code and the 15K agent training trajectory dataset (a subset of the full training data) are released together.
• [2025/12/31] We open-sourced S1-DeepResearch-8B-Preview, focusing on general long-chain complex reasoning and exploring what is feasible in deep research at a smaller parameter scale.

📝 Overview

S1-DeepResearch-32B is an end-to-end model developed by the ScienceOne AI for long-horizon deep research. Its core capabilities span five dimensions:

• Long-chain complex reasoning: Supports sustained reasoning and action across multi-stage, multi-hop tasks, going beyond single-step Q&A. Through cross-document retrieval, evidence aggregation, state memory, and policy iteration, it plans paths, integrates information, and converges results in complex settings, keeping the reasoning process stable and conclusions reliable.

• Deep research instruction following: Parses multi-constraint instructions in deep research scenarios and builds an instruction-understanding paradigm along the full research chain—task definition → mechanisms → tool execution → result presentation—with coordinated constraints across cognition, artifacts, execution, and environment so complex tasks stay controllable, processes predictable, and outputs aligned with intent.

• Deep research report writing: Produces arguable, citable report-style outputs on top of information integration; organizes multi-source material and evidence checks while balancing structure, readability, and traceability—suited for scientific writing and decision support.

• File understanding and generation: Covers PDFs, tables, web pages, and other modalities for input understanding, plus structured, deliverable outputs. In multi-turn tool-augmented interaction, it keeps semantics and execution aligned, closing the loop parse → process → generate and reducing repetitive manual work in research and data-heavy workflows.

• Skills Using: Organizes literature search, data analysis, experiment design, computational modeling, visualization, report generation, and more as callable modules, dynamically assembled and progressively loaded toward task goals, supporting continuous workflows from data acquisition to presentation.

✨ Key Features

• Ultra-long context modeling: A 128K context window lets a single session hold longer evidence chains and multi-turn interaction history, suited to long-horizon research tasks.
• Long-horizon tool calling: Stably runs 150+ consecutive tool-call rounds, building reasoning-driven tool orchestration and a decision closed loop—enabling continuous planning, execution, and self-correction across multi-stage tasks.
• Native tool ecosystem: 9 built-in common tools (e.g., search, web browsing, code execution, command line) ready to use out of the box.

🚀 Model Download

Model	Parameters	Context length	Download
S1-DeepResearch-32B	32B	128k	🤗 HuggingFace | 🤖 ModelScope
S1-DeepResearch-8B-Preview	8B	128k	🤗 HuggingFace | 🤖 ModelScope

📊 Evaluation

We systematically evaluated S1-DeepResearch-32B on 20 agentic capability benchmarks grouped into 5 dimensions aligned with the five capability areas:

• Long-chain complex reasoning: Text—GAIA (text), BrowseComp, BrowseComp-ZH, XBench-DeepSearch, HLE (text); vision-language—LiveVQA, MM-Search, BrowseComp-VL, RealX-Bench, HLE-VL, MM-BrowseComp.
• Deep research instruction following: ComplexBench, DeepResearchIF (in-house).
• Deep research report writing: DeepResearch Bench, DeepResearch Bench II, Research Rubrics.
• File understanding and generation: GAIA (file), GTA, FileSys (in-house).
• Skills Using: SkillsUse (in-house).

S1-DeepResearch-32B gains a clear advantage over the base Qwen3-32B and the larger Qwen3-235B on all listed benchmarks; on in-house leaderboards for deep-research instruction following, file understanding and generation, and skill invocation, it also surpasses Qwen3.5-397B. Overall performance is close to mainstream closed-source flagships (GPT 5.2, Claude 4.6, GLM-5, Kimi-K2.5). Public benchmarks and internal tasks are mutually consistent, indicating that S1-DeepResearch-32B is ready for real business deployment.

📂 Example Cases

Below is an example of S1-DeepResearch-32B using skills: during materials modeling, the model first invokes the scientific skill scientific-skills/pymatgen for domain knowledge, then follows the skill guidance to run modeling with pymatgen and outputs a CIF file.

More cases will be added under the cases/ directory.

🚀 Quick Start

Environment setup

1. Install dependencies:

pip install -r requirements.txt

2. Docker setup

The project provides official pre-built Docker images for fast deployment. There are two core images:

• toolkits-api: Main tool-service container (exposes API capabilities)
• code-sandbox: Code-execution sandbox image (created on demand by the service for isolated runs)

Execution-oriented tools (execute_code, bash) use Docker-outside-of-Docker (DooD): by mounting the host Docker socket, the tool container talks to the host Docker daemon and creates isolated sandbox containers as needed.

Image tags:

ghcr.io/wenge-research/toolkits-api:v2.0.260403
ghcr.io/wenge-research/code-sandbox:v1.0.260403

Pull images:

docker pull ghcr.io/wenge-research/toolkits-api:v2.0.260403
docker pull ghcr.io/wenge-research/code-sandbox:v1.0.260403

Run the container

Mount src/config.yaml, the Docker socket (for sandbox execution), and optionally log and cache directories:

docker run -d \
  --name toolkits-api \
  --network host \
  -e API_PORT=8080 \
  -e API_WORKERS=4 \
  -e HOST_LOG_DIR=$(pwd)/logs \
  -e SANDBOX_MODE=docker \
  -e HTTP_PROXY=http://your-proxy:port \
  -e HTTPS_PROXY=http://your-proxy:port \
  -e PROXY_URL=http://your-proxy:port \
  -v /etc/localtime:/etc/localtime:ro \
  -v /etc/timezone:/etc/timezone:ro \
  -v /var/run/docker.sock:/var/run/docker.sock \
  -v $(pwd)/src/config.yaml:/app/src/config.yaml \
  -v $(pwd)/logs:/app/logs \
  -v $(pwd)/cache:/app/cache \
  ghcr.io/wenge-research/toolkits-api:v2.0.260403

Parameter reference

Flag / env	Description
-e API_PORT	Listen port, default 8080
-e API_WORKERS	Number of worker processes; tune for concurrency, default 1
-e SANDBOX_MODE=docker	Enable Docker sandbox mode (otherwise subprocess)
-e HOST_LOG_DIR	Host log directory for sandbox mounts when Docker sandbox is enabled
-e HTTP_PROXY / HTTPS_PROXY / PROXY_URL	Proxy settings (optional)
--network host	Use if you rely on a proxy bound on the host (optional)
-v /etc/localtime:/etc/localtime:ro	Sync host timezone (read-only)
-v /etc/timezone:/etc/timezone:ro	Sync host timezone file (read-only)
-v /var/run/docker.sock	Required for Docker sandbox mode to schedule sandbox containers
-v config.yaml	Mount config (API keys, model and sandbox settings)
-v logs	Mount log directory (optional)
-v cache	Mount cache directory; structure mirrors /app/cache inside the container (optional)

3. Configure the tool service URL

Prefer JSON config or environment variables to override defaults. Avoid editing utils/configs.py directly.

Option A (recommended): local JSON

Copy from the example and edit locally:

cp utils/config/config.example.json utils/config/config.local.json

Set the tool service base URL in utils/config/config.local.json, for example:

{
  "TOOLS_SERVER_BASE_ENDPOINT_URL": [
    "http://127.0.0.1:8080"
  ]
}

Option B: environment variables

Point to a config file or override individual keys:

export S1_DR_CONFIG_JSON="utils/config/config.local.json"
# or override TOOLS_SERVER_BASE_ENDPOINT_URL only
export TOOLS_SERVER_BASE_ENDPOINT_URL='["http://127.0.0.1:8080"]'

4. API keys

Prefer utils/config/config.local.json for provider keys, or mirror the same names with environment variables:

{
  "AIHUBMIX_KEY": "<your_aihubmix_key>",
  "AZURE_KEY": "<your_azure_key>",
  "VOLCANO_KEY": "<your_volcano_key>",
  "ALIYUN_KEY": "<your_aliyun_key>"
}

Environment variables:

export AIHUBMIX_KEY="<your_aihubmix_key>"
export AZURE_KEY="<your_azure_key>"
export VOLCANO_KEY="<your_volcano_key>"
export ALIYUN_KEY="<your_aliyun_key>"

Single-query inference

import asyncio

from server.llm_api import LLMClient
from server.tool_api import return_all_tools
from inference.run_single_inference import run_one_query
from utils.prompts import DEEPRESEARCH_SYSTEM_PROMPT


async def main():
    llm_client_urls = ["http://127.0.0.1:10777/v1/chat/completions"]
    llm_client_models = ["S1-DeepResearch-32B"]
    llm_client = LLMClient(llm_client_urls, llm_client_models)

    all_tools = return_all_tools()

    result = await run_one_query(
        llm=llm_client,
        user_query="阿里巴巴成立时，18位创始团队成员中，姓马、姓蔡、姓张的创始人的平均年龄，保留一位小数",
        file_path=[],
        system=DEEPRESEARCH_SYSTEM_PROMPT,
        max_rounds=15,
        temperature=0.4,
        top_p=0.95,
        extra_payload={},
        debug=True,
        all_tools=all_tools,
        system_format="deep_research",
        log_label="quick_start_single",
    )

    final_answer = result[-1]["final_answer"] if result else ""
    print(final_answer)


if __name__ == "__main__":
    asyncio.run(main())

Notes:

• file_path must be a list in the current implementation (e.g. [] or ['/path/a.pdf']).
• system_format options: deep_research, azure, aihubmix, aihubmix_claude, aihubmix_glm, volcano, aliyun.

Batch inference

Local / vLLM:

cd inference
cp run_batch_inference_demo.sh run_batch_local.sh
# Edit run_batch_local.sh (LLM_CLIENT_URLS, LLM_CLIENT_MODELS, TEST_DATA_FILE, etc.)
bash run_batch_local.sh

Hosted APIs:

cd inference
cp run_batch_inference_online_demo.sh run_batch_online.sh
# Edit run_batch_online.sh (LLM_CLIENT_URLS, LLM_CLIENT_MODELS, SYSTEM_FORMAT, etc.)
bash run_batch_online.sh

Logs:

tail -f run_logs/*.log

📖 Advanced usage.

🔭 Future Work

• S1-DeepResearch Paper: We expect to release the paper within about two weeks, covering data synthesis for the five capability areas, training and inference design, test-time scaling, and key evaluation takeaways.
• S1-DeepResearch-VL: In the first half of 2026, we plan to release S1-DeepResearch-VL with vision understanding and cross-modal reasoning for richer research-style tasks.

📜 License

This project is licensed under the Apache License 2.0.

Citation

If S1-DeepResearch is useful to your work, please consider citing:

@software{s1deepresearch2026,
    title={S1-DeepResearch: End-to-End Deep Research Models},
    author={ScienceOne Team},
    year={2026},
    url={https://github.com/ScienceOne-AI/S1-DeepResearch},
}