🧬 Thoth

Thoth is a lightweight version of Thoth, designed for efficient and scalable biological protocol generation while retaining strong scientific reasoning ability.

📄 Paper: Unleashing Scientific Reasoning for Bio-experimental Protocol Generation via Structured Component-based Reward Mechanism (ICLR 2026)
🔗 GitHub: https://github.com/manglu097/Thoth
🤗 Dataset: https://huggingface.co/datasets/manglu3935/SciRecipe

🔍 Model Overview

Base model: Qwen3-8B
Parameters: 8B
GPU memory: ~16GB
Primary task: Biological experimental protocol generation

Thoth is trained with the same Sketch-and-Fill paradigm and SCORE reward mechanism as Thoth, offering a strong performance–efficiency trade-off.

🧠 Output Format

<think>  reasoning and planning </think>
<key>    structured machine-readable steps </key>
<orc>    natural language protocol </orc>
<note>   optional safety notes </note>

🚀 Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("manglu3935/Thoth")
model = AutoModelForCausalLM.from_pretrained("manglu3935/Thoth")

⚠️ Intended Use

For fast scientific reasoning experiments and scalable research deployment.
Generated protocols must be reviewed by qualified experts prior to laboratory execution.

📖 Citation

@article{sun2025unleashing,
  title={Unleashing Scientific Reasoning for Bio-experimental Protocol Generation via Structured Component-based Reward Mechanism},
  author={Sun, Haoran and Jiang, Yankai and Tang, Zhenyu and others},
  journal={arXiv preprint arXiv:2510.15600},
  year={2025}
}