| --- |
| license: apache-2.0 |
| language: |
| - en |
| - zh |
| base_model: Qwen/Qwen3-8B |
| library_name: transformers |
| tags: |
| - deep-research |
| - agent |
| - information-seeking |
| - web-search |
| - verification |
| - react |
| pipeline_tag: text-generation |
| model-index: |
| - name: Marco-DeepResearch-8B |
| results: |
| - task: |
| type: question-answering |
| name: BrowseComp |
| dataset: |
| name: BrowseComp |
| type: browsecomp |
| metrics: |
| - type: accuracy |
| value: 31.4 |
| name: Accuracy |
| - task: |
| type: question-answering |
| name: BrowseComp-ZH |
| dataset: |
| name: BrowseComp-ZH |
| type: browsecomp-zh |
| metrics: |
| - type: accuracy |
| value: 47.1 |
| name: Accuracy |
| - task: |
| type: question-answering |
| name: GAIA (text-only) |
| dataset: |
| name: GAIA |
| type: gaia |
| metrics: |
| - type: accuracy |
| value: 69.9 |
| name: Accuracy |
| - task: |
| type: question-answering |
| name: xBench-DeepSearch-2505 |
| dataset: |
| name: xBench-DeepSearch-2505 |
| type: xbench-deepsearch |
| metrics: |
| - type: accuracy |
| value: 82.0 |
| name: Accuracy |
| - task: |
| type: question-answering |
| name: WebWalkerQA |
| dataset: |
| name: WebWalkerQA |
| type: webwalkerqa |
| metrics: |
| - type: accuracy |
| value: 69.6 |
| name: Accuracy |
| --- |
| |
| # Marco-DeepResearch-8B |
|
|
| <p align="center"> |
| <a href="https://arxiv.org/abs/2603.28376">📄 Paper</a> • |
| <a href="https://github.com/AIDC-AI/Marco-DeepResearch/tree/main/Marco-DeepResearch-Family/Marco-Agent-DeepResearch">💻 GitHub</a> |
| </p> |
|
|
| ## Introduction |
|
|
| **Marco DeepResearch** is an efficient 8B-scale deep research agent developed by **Alibaba International Digital Commerce (AIDC-AI)**. It autonomously conducts open-ended investigations by integrating complex information retrieval with multi-step reasoning across diverse web sources. |
|
|
| Marco DeepResearch is optimized through a **verification-centric framework** at three levels: |
|
|
| 1. **Verified Data Synthesis** — Graph-based and agent-based QA synthesis with explicit verification to control difficulty and ensure answer uniqueness/correctness. |
| 2. **Verification-Driven Trajectory Construction** — Multi-agent framework (main agent + search sub-agent + verifier sub-agent) that injects explicit verification patterns into training trajectories. |
| 3. **Verifier-Guided Test-Time Scaling** — Uses the agent itself as a verifier at inference time, achieving +12.1 avg. improvement on benchmarks. |
|
|
| Under a maximum budget of 600 tool calls, Marco DeepResearch significantly outperforms 8B-scale agents and surpasses or approaches several 30B-scale agents (e.g., Tongyi DeepResearch-30B) on challenging benchmarks. |
|
|
| ## Model Details |
|
|
| | Attribute | Details | |
| |---|---| |
| | **Base Model** | [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) | |
| | **Parameters** | ~8B | |
| | **Context Window** | 128K tokens (extended via YaRN) | |
| | **Training** | SFT + RL (GRPO) | |
| | **Training Hardware** | 64 × NVIDIA A100 GPUs | |
| | **Max Generation Length** | 16,384 tokens | |
| | **Decoding** | Temperature 0.7, Top-p 0.95 | |
| | **Max Tool Calls** | 600 (evaluation budget) | |
|
|
| ## Prompt Format |
|
|
| ### Prompt Structure |
|
|
| The prompt follows a **System + User** two-part design (similar to OpenAI native function calling): |
|
|
| - **System Prompt** = Role definition + Output format + Current date + Tool definitions |
| - **User Prompt** = Only the user question |
|
|
| This ensures tool definitions stay at the front of context and won't be diluted by long multi-turn conversations. |
|
|
| ### System Prompt Template |
|
|
| Below is the complete system prompt. Replace `{current_date}` with the actual date and `{tools_json}` with your tool definitions. |
|
|
| ``` |
| You are an expert web researcher. Your task is to find accurate, complete answers through iterative search, extraction, and verification. |
| |
| ## Core Principles |
| |
| 1) Strategic Planning |
| - Decompose complex questions into targeted sub-tasks |
| - Choose the right tool for each step |
| - Refine your approach based on what you learn |
| |
| 2) Precise Execution |
| - Define clear objectives before using any tool |
| - Provide sufficient detail for accurate results |
| - Avoid vague or overly broad requests |
| |
| 3) Rigorous Verification |
| - Cross-check important facts across multiple sources |
| - Resolve conflicts by gathering additional evidence |
| - Only conclude when evidence is sufficient and consistent |
| |
| ## Output Format |
| |
| In each turn, you can either call a tool or provide the final answer. |
| |
| **Call a tool:** |
| <think>your reasoning process</think> |
| <tool_call> |
| {"name": "tool_name", "arguments": {"param1": "value1", "param2": "value2"}} |
| </tool_call> |
| |
| **Provide final answer (when you have gathered enough information):** |
| <think>your reasoning and analysis</think> |
| <answer>the direct answer to the question</answer> |
| |
| Note: All reasoning should be in <think>, <answer> should contain only the final answer. |
| |
| Current date: {current_date} |
| |
| # Tools |
| |
| You may call one or more functions to assist with the user query. |
| |
| You are provided with function signatures within <tools></tools> XML tags: |
| <tools> |
| {tools_json} |
| </tools> |
| |
| For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags: |
| <tool_call> |
| {"name": <function-name>, "arguments": <args-json-object>} |
| </tool_call> |
| ``` |
|
|
| ### Tool Definition Format |
|
|
| Tools use the **OpenAI function calling format** and are placed inside the system prompt. Example: |
|
|
| ```json |
| [ |
| { |
| "type": "function", |
| "function": { |
| "name": "search", |
| "description": "Search the web via Google to find relevant information and URLs.", |
| "parameters": { |
| "type": "object", |
| "properties": { |
| "querys": { |
| "type": "array", |
| "items": {"type": "string"}, |
| "description": "Search queries for finding relevant information. Supports single or multiple queries." |
| } |
| }, |
| "required": ["querys"] |
| } |
| } |
| }, |
| { |
| "type": "function", |
| "function": { |
| "name": "visit", |
| "description": "Read webpage content to extract specific information, verify claims, or understand context.", |
| "parameters": { |
| "type": "object", |
| "properties": { |
| "urls": { |
| "type": "array", |
| "items": {"type": "string"}, |
| "description": "URL(s) to visit. Supports single or multiple urls." |
| }, |
| "goal": { |
| "type": "string", |
| "description": "The specific information to retrieve. Be precise, not vague." |
| } |
| }, |
| "required": ["urls", "goal"] |
| } |
| } |
| } |
| ] |
| ``` |
|
|
| ### Model Output Format |
|
|
| In each turn, the model produces one of two structured outputs: |
|
|
| **Tool call turn:** |
| ```xml |
| <think> |
| I need to search for information about X to answer the user's question. |
| Let me start by searching for... |
| </think> |
| <tool_call> |
| {"name": "search", "arguments": {"querys": ["search query here"]}} |
| </tool_call> |
| ``` |
|
|
| **Final answer turn:** |
| ```xml |
| <think> |
| Based on the evidence gathered from multiple sources, I can now conclude that... |
| Let me verify: Source A says X, Source B confirms X, and Source C also supports X. |
| </think> |
| <answer> |
| The direct answer to the question. |
| </answer> |
| ``` |
|
|
| ### Multi-Turn Conversation Format |
|
|
| A complete multi-turn agent session looks like: |
|
|
| ``` |
| [System] Role + Tools + Date |
| |
| [User] What is the population of the largest city in Switzerland? |
| |
| [Asst] <think>I need to find the largest city in Switzerland first...</think> |
| <tool_call>{"name": "search", "arguments": {"querys": ["largest city in Switzerland"]}}</tool_call> |
| |
| [User] <tool_response>{"results": [{"title": "Zürich - Wikipedia", ...}]}</tool_response> |
| |
| [Asst] <think>Zürich is the largest city. Now let me find its population...</think> |
| <tool_call>{"name": "visit", "arguments": {"urls": ["..."], "goal": "population of Zürich"}}</tool_call> |
| |
| [User] <tool_response>{"found": true, "content": "Population: 434,335 (2024)"}</tool_response> |
| |
| [Asst] <think>I found the answer with a reliable source...</think> |
| <answer>The largest city in Switzerland is Zürich, with a population of approximately 434,335.</answer> |
| ``` |
|
|
| ## Quick Start |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import json |
| from datetime import datetime |
| |
| model_name = "AIDC-AI/Marco-DeepResearch-8B" |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) |
| model = AutoModelForCausalLM.from_pretrained( |
| model_name, |
| torch_dtype="auto", |
| device_map="auto", |
| trust_remote_code=True, |
| ) |
| |
| # 1. Define tools (OpenAI function calling format) |
| tools = [ |
| { |
| "type": "function", |
| "function": { |
| "name": "search", |
| "description": "Search the web via Google to find relevant information and URLs.", |
| "parameters": { |
| "type": "object", |
| "properties": { |
| "querys": { |
| "type": "array", |
| "items": {"type": "string"}, |
| "description": "Search queries for finding relevant information. Supports single or multiple queries." |
| } |
| }, |
| "required": ["querys"] |
| } |
| } |
| }, |
| { |
| "type": "function", |
| "function": { |
| "name": "visit", |
| "description": "Read webpage content to extract specific information, verify claims, or understand context.", |
| "parameters": { |
| "type": "object", |
| "properties": { |
| "urls": { |
| "type": "array", |
| "items": {"type": "string"}, |
| "description": "URL(s) to visit. Supports single or multiple urls." |
| }, |
| "goal": { |
| "type": "string", |
| "description": "The specific information to retrieve. Be precise, not vague." |
| } |
| }, |
| "required": ["urls", "goal"] |
| } |
| } |
| } |
| ] |
| |
| # 2. Build system prompt |
| ROLE_PROMPT = """You are an expert web researcher. Your task is to find accurate, complete answers through iterative search, extraction, and verification. |
| |
| ## Core Principles |
| |
| 1) Strategic Planning |
| - Decompose complex questions into targeted sub-tasks |
| - Choose the right tool for each step |
| - Refine your approach based on what you learn |
| |
| 2) Precise Execution |
| - Define clear objectives before using any tool |
| - Provide sufficient detail for accurate results |
| - Avoid vague or overly broad requests |
| |
| 3) Rigorous Verification |
| - Cross-check important facts across multiple sources |
| - Resolve conflicts by gathering additional evidence |
| - Only conclude when evidence is sufficient and consistent |
| |
| ## Output Format |
| |
| In each turn, you can either call a tool or provide the final answer. |
| |
| **Call a tool:** |
| <think>your reasoning process</think> |
| <tool_call> |
| {"name": "tool_name", "arguments": {"param1": "value1", "param2": "value2"}} |
| </tool_call> |
| |
| **Provide final answer (when you have gathered enough information):** |
| <think>your reasoning and analysis</think> |
| <answer>the direct answer to the question</answer> |
| |
| Note: All reasoning should be in <think>, <answer> should contain only the final answer.""" |
| |
| current_date = datetime.now().strftime("%Y-%m-%d") |
| tools_json = "\n".join([json.dumps(t, ensure_ascii=False) for t in tools]) |
| |
| system_prompt = f"""{ROLE_PROMPT} |
| |
| Current date: {current_date} |
| |
| # Tools |
| |
| You may call one or more functions to assist with the user query. |
| |
| You are provided with function signatures within <tools></tools> XML tags: |
| <tools> |
| {tools_json} |
| </tools> |
| |
| For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags: |
| <tool_call> |
| {{"name": <function-name>, "arguments": <args-json-object>}} |
| </tool_call>""" |
| |
| # 3. Build messages and generate |
| messages = [ |
| {"role": "system", "content": system_prompt}, |
| {"role": "user", "content": "Who won the 2026 Turing Award?"}, |
| ] |
| |
| text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
| inputs = tokenizer([text], return_tensors="pt").to(model.device) |
| outputs = model.generate(**inputs, max_new_tokens=16384, temperature=0.7, top_p=0.95) |
| response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True) |
| print(response) |
| ``` |
|
|
| > For the full **multi-turn agent loop**, see the [GitHub repository](https://github.com/AIDC-AI/Marco-DeepResearch/tree/main/Marco-DeepResearch-Family/Marco-Agent-DeepResearch). |
|
|
| ## Benchmark Results |
|
|
| Evaluated on a suite of deep search benchmarks under a maximum budget of **600 tool calls**. |
|
|
| <p align="center"> |
| <img src="https://raw.githubusercontent.com/AIDC-AI/Marco-DeepResearch/refs/heads/main/Marco-DeepResearch-Family/Marco-Agent-DeepResearch/assets/benchmark_chart_v2.png" alt="Marco DeepResearch benchmark performance across BrowseComp, BrowseComp-ZH, xBench-DeepSearch-2510, and GAIA (text-only)" width="100%" /> |
| </p> |
|
|
| | Model | BrowseComp | BrowseComp-ZH | GAIA (text-only) | WebWalkerQA | xBench-DS-2505 | xBench-DS-2510 | DeepSearchQA | HLE-Text | |
| |---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
| | *Foundation Models with Tools* | | | | | | | | | |
| | GLM-4.7 | 67.5 | 66.6 | 61.9 | – | 72.0 | 52.3 | – | 42.8 | |
| | Minimax-M2.1 | 62.0 | 47.8 | 64.3 | – | 68.7 | 43.0 | – | 19.1 | |
| | DeepSeek-V3.2 | 67.6 | 65.0 | 75.1 | – | 78.0 | 55.7 | 60.9 | 40.8 | |
| | Kimi-K2.5 | 74.9 | 62.3 | – | – | – | 46.0 | 77.1 | – | |
| | Claude-4-Sonnet | 12.2 | 29.1 | 68.3 | 61.7 | 64.6 | – | – | – | |
| | Claude-4.5-Opus | 67.8 | 62.4 | – | – | – | – | 80.0 | – | |
| | OpenAI-o3 | 49.7 | 58.1 | – | 71.7 | 67.0 | – | – | – | |
| | OpenAI GPT-5 High | 54.9 | 65.0 | 76.4 | – | 77.8 | 75.0 | 79.0 | – | |
| | Gemini-3.0-Pro | 59.2 | 66.8 | – | – | – | 53.0 | 76.9 | – | |
| | *Trained Agents (≥30B)* | | | | | | | | | |
| | MiroThinker-v1.7-mini | 67.9 | 72.3 | 80.3 | – | – | 57.2 | 67.9 | 36.4 | |
| | MiroThinker-v1.5-235B | 69.8 | 71.5 | 80.8 | – | 77.1 | – | – | 39.2 | |
| | MiroThinker-v1.5-30B | 56.1 | 66.8 | 72.0 | – | 73.1 | – | – | 31.0 | |
| | MiroThinker-v1.0-72B | 47.1 | 55.6 | 81.9 | 62.1 | 77.8 | – | – | 37.7 | |
| | MiroThinker-v1.0-30B | 41.2 | 47.8 | 73.5 | 61.0 | 70.6 | – | – | 33.4 | |
| | SMTL-30B-300 | 48.6 | – | 75.7 | 76.5 | 82.0 | – | – | – | |
| | Tongyi-DR-30B | 43.4 | 46.7 | 70.9 | 72.2 | 75.0 | 55.0 | – | 32.9 | |
| | WebSailor-V2-30B | 35.3 | 44.1 | 74.1 | – | 73.7 | – | – | – | |
| | DeepMiner-32B-RL | 33.5 | 40.1 | 58.7 | – | 62.0 | – | – | – | |
| | OpenSeeker-30B-SFT | 29.5 | 48.4 | – | – | 74.0 | – | – | – | |
| | *Trained Agents (≤8B)* | | | | | | | | | |
| | AgentCPM-Explore-4B | 24.1 | 29.1 | 63.9 | 68.1 | 70.0 | 34.0* | 32.8* | 19.1 | |
| | WebExplorer-8B-RL | 15.7 | 32.0 | 50.0 | 62.7 | 53.7 | 23.0* | 17.8* | 17.3 | |
| | RE-TRAC-4B | 30.0 | 36.1 | 70.4 | – | 76.6 | – | – | 22.2 | |
| | MiroThinker-v1.0-8B | 31.1 | 40.2 | 66.4 | 60.6 | 60.6 | 34.0* | 36.7* | 21.5 | |
| | **Marco-DR-8B (Ours)** | **31.4** | **47.1** | **69.9** | **69.6** | **82.0** | **42.0** | **29.9** | **22.5** | |
|
|
| > \* marks scores we reproduced with our own implementation; other scores are from the respective official reports. |
|
|
| ## Intended Use |
|
|
| Marco DeepResearch is designed for: |
| - **Open-ended web research** — Autonomously investigating complex questions across multiple web sources |
| - **Multi-hop question answering** — Solving questions that require chaining information from multiple documents |
| - **Information seeking** — Navigating the web to locate specific, hard-to-find information |
| - **Fact verification** — Verifying claims through multi-source evidence aggregation |
|
|
| ## Limitations |
|
|
| - Performance depends on the quality and accessibility of external web search tools and APIs. |
| - The model is optimized for information-seeking tasks and may not generalize to all types of reasoning tasks. |
| - Enabling test-time scaling introduces extra inference overhead; the number of tool-call rounds can be tuned based on the use case. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{zhu2026marco, |
| title={Marco DeepResearch: Unlocking Efficient Deep Research Agents via Verification-Centric Design}, |
| author={Bin Zhu and Qianghuai Jia and Tian Lan and Junyang Ren and Feng Gu and Feihu Jiang and Longyue Wang and Zhao Xu and Weihua Luo}, |
| journal={arXiv preprint arXiv:2603.28376}, |
| year={2026} |
| } |
| ``` |
|
|
| ## License |
|
|
| This model is released under the [Apache 2.0 License](LICENSE). |
|
|
