Eubiota-Planner-8B
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 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 |
| 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 |
| PubMedQA | Medical-biology reasoning | qiaojin/PubMedQA |
| MedQA-USMLE | Medical-biology reasoning | GBaker/MedQA-USMLE-4-options |
| DeepMath-103K | Mathematical reasoning | zwhe99/DeepMath-103K |
| Natural Questions | Agentic search | 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 |
Quick Start
Framework Usage
For the complete agentic experience with tool integration:
# Install Eubiota
bash setup.sh
source .venv/bin/activate
# Configure API keys
cp scientist/.env.template scientist/.env
# Edit .env with your API keys
# Run inference
python scientist/solver_scientist.py
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 |
| WMDP-Bio | Expert-level capabilities in hazardous biological knowledge and biosecurity-relevant domains | Li et al., 2024 |
Microbiome-Specific Mechanistic Reasoning
Four domain-specific benchmarks derived from 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 |
Citation
@article{eubiota2026,
title={Eubiota: Agentic AI for Autonomous Microbiome Discovery},
author={Lu, Pan and Peng, William and Zhang, Haoxiang and Zhu, Kunlun and Xu, Qixin},
journal={arXiv preprint arXiv:TBD},
year={2026}
}
Acknowledgements
This work is supported by:
We thank the following open-source projects:
- VeRL for the RL framework
- vLLM for fast inference
- AgentFlow and Agent Lightning for multi-agent RL exploration
Contact
For questions and feedback, please open an issue on our GitHub repository or visit eubiota.ai.
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