---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-8B
pipeline_tag: text-generation
library_name: transformers
tags:
- microbiome
- agentic-ai
- scientific-reasoning
- tool-use
- multi-agent
- reinforcement-learning
- GRPO
datasets:
- Eubiota/microbio-bench
- qiaojin/PubMedQA
- GBaker/MedQA-USMLE-4-options
- zwhe99/DeepMath-103K
- RUC-NLPIR/FlashRAG_datasets
---

<div style="display: flex; align-items: center; justify-content: center; gap: 8px;">
  <a href="https://eubiota.ai/">
    <img src="https://huggingface.co/datasets/Eubiota/microbio-bench/resolve/main/logo_transparent.png" alt="Eubiota Logo" style="height: 84px; width: auto;">
  </a>
</div>

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<p align="center">
  🤗 <a href="https://huggingface.co/Eubiota" target="_blank">HuggingFace</a> ｜
  🌐 <a href="https://eubiota.ai/" target="_blank">Website</a> ｜
  📄 <a href="https://www.biorxiv.org/content/10.64898/2026.02.27.708412v1" target="_blank">Paper</a> ｜
  💻 <a href="https://github.com/lupantech/Eubiota" target="_blank">GitHub</a> ｜
  🚀 <a href="https://app.eubiota.ai/" target="_blank">App</a>
</p>

---

**Eubiota-Planner-8B** is a specialized 8-billion parameter language model fine-tuned for autonomous microbiome discovery. It serves as the core planner module in the [Eubiota](https://eubiota.ai/) agentic AI framework, orchestrating multi-agent reasoning and tool-grounded scientific exploration.


## Model Description

| Property | Details |
|----------|---------|
| **Model Type** | Causal language model fine-tuned for agentic planning |
| **Base Model** | [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) |
| **Training Method** | GRPO-MAS (Group Relative Policy Optimization for Multi-Agent Systems) |
| **Language** | English |
| **Developed By** | Stanford University |
| **Affiliations** | Department of Biomedical Data Science, Department of Computer Science, Department of Microbiology and Immunology, Institute for Human-Centered AI (HAI) |

## Intended Use

### Primary Use Cases

- **Microbiome Hypothesis Generation**: Formulating mechanistic hypotheses about host-microbe interactions
- **Experimental Design**: Planning and orchestrating scientific workflows for microbiome research  
- **Multi-Tool Coordination**: Selecting and sequencing domain-specific tools (PubMed, KEGG pathways, etc.) for complex scientific queries

## Training

### Training Data

The model was trained on a mixture of four domain datasets:

| Dataset | Purpose | Source |
|---------|---------|--------|
| **Microbio-Bench** | Curated microbiome reasoning | [Eubiota/microbio-bench](https://huggingface.co/datasets/Eubiota/microbio-bench) |
| **PubMedQA** | Medical-biology reasoning | [qiaojin/PubMedQA](https://huggingface.co/datasets/qiaojin/PubMedQA) |
| **MedQA-USMLE** | Medical-biology reasoning | [GBaker/MedQA-USMLE-4-options](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options) |
| **DeepMath-103K** | Mathematical reasoning | [zwhe99/DeepMath-103K](https://huggingface.co/datasets/zwhe99/DeepMath-103K) |
| **Natural Questions** | Agentic search | [RUC-NLPIR/FlashRAG_datasets](https://huggingface.co/datasets/RUC-NLPIR/FlashRAG_datasets) |

### Training Method

The model is trained using **GRPO-MAS (Group Relative Policy Optimization for Multi-Agent Systems)**, a reinforcement learning approach designed for multi-agent coordination. This method enables the planner to learn effective tool selection and sequencing strategies through outcome-driven refinement.

### Integrated Tools

Eubiota-Planner-8B orchestrates **18 domain-specific tools** spanning web and literature search, biological databases, laboratory resources, and computation utilities.


| Category | Tools | Description |
|----------|-------|-------------|
| **Web Search** | Google Search, Perplexity Search, Wikipedia Search, URL Page Retrieval | Grounded web search with citation support |
| **Literature Search** | PubMed Search | Retrieves relevant papers from PubMed and produces citation-grounded summaries |
| **KEGG Databases** | KEGG Gene, KEGG Drug, KEGG Disease, KEGG Organism | Query tools for genes, drugs, diseases, and organisms |
| **MDIPID Databases** | MDIPID Gene View, MDIPID Disease View, MDIPID Microbe View | Microbiota-drug-disease association queries from multiple perspectives |
| **Laboratory Tools** | Transposon Screen Database, Protocol Library | Access to pre-computed gene-phenotype rankings (1,836 genes) and 55 curated microbiome/immunology experimental protocols |
| **Document Utilities** | Doc Context Search, Database Context Search | File and document context retrieval |
| **Computation** | Python Execution, LLM Summarization | Code execution and text synthesis capabilities |

## Benchmark Suite

Eubiota-Planner-8B is evaluated on **six benchmarks** across two categories to assess both broad biomedical competence and microbiome-specific mechanistic reasoning.

### General Biomedical Competence

| Benchmark | Description | Source |
|-----------|-------------|--------|
| **MedMCQA (Medicine)** | Professional clinical knowledge and complex diagnostic reasoning from medical entrance examinations | [Pal et al., 2022](https://aclanthology.org/2022.chemnlp-1.6/) |
| **WMDP-Bio** | Expert-level capabilities in hazardous biological knowledge and biosecurity-relevant domains | [Li et al., 2024](https://arxiv.org/abs/2403.03218) |

### Microbiome-Specific Mechanistic Reasoning

Four domain-specific benchmarks derived from [MDIPID](https://www.omic.tech/mdipid/) emphasizing mechanistic linking among microbes, drugs, host pathways, and genes:

| Benchmark | Task | Description |
|-----------|------|-------------|
| **Drug-Microbe Impact (Drug-Imp)** | Drug → Microbe | Determining the directional effects of drugs on microbial growth |
| **Microbe-Protein Mechanism (MB-Mec)** | Microbe → Protein | Linking microbial activity to host proteins |
| **Protein Functional Comprehension (Prot-Func)** | Protein → Function | Identifying the biological function of a protein |
| **Protein-Gene Mapping (Prot-Gen)** | Protein → Gene | Mapping expressed proteins to their encoding genes |


## Acknowledgements

<div style="display: flex; align-items: center; justify-content: center; gap: 8px;">
  <a href="https://chanzuckerberg.com/" style="flex-shrink: 0;">
    <img src="assets/logos/chan_zuckberg.svg" style="height: 75px; width: auto;" alt="Chan Zuckerberg Initiative">
  </a>
  
  <a href="https://hai.stanford.edu/" style="flex-shrink: 0;">
    <img src="assets/logos/stanford_hai.png" style="height: 76px; width: auto;" alt="Stanford HAI">
  </a>
  
  <a href="https://www.renaissancephilanthropy.org/" style="flex-shrink: 0;">
    <img src="assets/logos/Renaissance.png" style="height: 84px; width: auto;" alt="Renaissance Philanthropy">
  </a>
  
  <a href="https://deepmind.google/technologies/gemini/" style="flex-shrink: 0;">
    <img src="assets/logos/Gemini.png" style="height: 84px; width: auto;" alt="Google Gemini">
  </a>
</div>

We also thank the following open-source projects:
- [VeRL](https://github.com/volcengine/verl) for the excellent RL framework design.
- [vLLM ](https://github.com/vllm-project/vllm) for fast LLM inference support.
- [AgentFlow](https://github.com/lupantech/AgentFlow) and [Agent Lightning](https://github.com/microsoft/agent-lightning) for early-stage exploration in multi-agent RL training.

## Citation
```bibtex
@article{lu2026eubiota,
  title = {Eubiota: Modular Agentic AI for Autonomous Discovery in the Gut Microbiome},
  author = {Lu, Pan and Gao, Yifan and Peng, William G. and Zhang, Haoxiang and Zhu, Kunlun and Robinson, Elektra K. and Xu, Qixin and Kotaka, Masakazu and Zhang, Harrison G. and Li, Bingxuan and Shiver, Anthony L. and Choi, Yejin and Huang, Kerwyn Casey and Sonnenburg, Justin and Zou, James},
  journal = {bioRxiv},
  year = {2026},
  month = {feb},
  day = {27},
  doi = {10.64898/2026.02.27.708412},
  url = {https://www.biorxiv.org/content/10.64898/2026.02.27.708412v1},
  publisher = {Cold Spring Harbor Laboratory}
}

```