CycleResearcher is a comprehensive open-source ecosystem for AI-powered academic research and review. Our system features three integrated components:
- CycleResearcher: Generates high-quality research papers
- CycleReviewer: Provides detailed academic reviews
- DeepReviewer: Delivers multi-perspective review simulations with self-verification
By creating a complete feedback loop between research generation and evaluation, we aim to:
- 🤖 Automate academic research processes
- 📝 Provide rigorous, multi-perspective research reviews
- 🔄 Establish research-review feedback loops
- 🚀 Accelerate scientific discovery
pip install ai_researcher# Import necessary libraries
from ai_researcher import CycleResearcher
from ai_researcher.utils import print_paper_summary
# Initialize CycleResearcher with default 12B model
researcher = CycleResearcher(model_size="12B")
# Load references from BibTeX file
with open('cycleresearcher_references.bib', 'r') as f:
references_content = f.read()
# Generate paper with specific references
generated_papers = researcher.generate_paper(
topic="AI Researcher",
references=references_content,
n=1 # Generate a single paper
)
# Print summary of generated paper
print_paper_summary(generated_papers[0])# Import necessary libraries
from ai_researcher import CycleReviewer
# Initialize CycleReviewer with default 8B model
reviewer = CycleReviewer(model_size="8B")
# Review a paper (assuming paper_text contains the paper content)
review_results = reviewer.evaluate(paper_text)
# Print review results
print(f"Average score: {review_results[0]['avg_rating']}")
print(f"Decision: {review_results[0]['paper_decision']}")# Import necessary libraries
from ai_researcher import DeepReviewer
# Initialize DeepReviewer with 14B model
deep_reviewer = DeepReviewer(model_size="14B")
# Review a paper with multiple simulated reviewers in Standard Mode
review_results = deep_reviewer.evaluate(
paper_text,
mode="Standard Mode", # Options: "Fast Mode", "Standard Mode", "Best Mode"
reviewer_num=4 # Simulate 4 different reviewers
)
# Print review results
for i, review in enumerate(review_results[0]['reviews']):
print(f"Reviewer {i+1} Rating: {review.get('rating', 'N/A')}")
print(f"Reviewer {i+1} Summary: {review.get('summary', 'N/A')[:100]}...")
CycleResearcher-12B achieves an average score of 5.36, approaching the 5.69 average for conference-accepted papers and surpassing AI Scientist's score of 4.31.
CycleReviewer outperforms both proprietary systems and human experts with a 48.77% reduction in Proxy MSE and a 26.89% reduction in Proxy MAE compared to human reviewers. With a decision accuracy of 74.24%, our model demonstrates a significant lead over other closed-source systems.
CycleResearcher Models
| Model Name | Pre-training Language Model | HF Link |
|---|---|---|
| CycleResearcher-ML-12B | Mistral-Nemo-Instruct-2407 | 🤗 link |
| CycleResearcher-ML-72B | Qwen2.5-72B-Instruct | 🤗 link |
| CycleResearcher-ML-123B | Mistral-Large-2 | 🤗 link |
CycleReviewer Models
| Model Name | Pre-training Language Model | HF Link |
|---|---|---|
| CycleReviewer-ML-Llama3.1-8B | Llama3.1-8B-Instruct | 🤗 link |
| CycleReviewer-ML-Llama3.1-70B | Llama3.1-70B-Instruct | 🤗 link |
| CycleReviewer-ML-Pro-123B | Mistral-Large-2 | 🤗 link |
DeepReviewer Models
| Model Name | Parameters | HF Link |
|---|---|---|
| DeepReviewer-7B | 7B | 🤗 link |
| DeepReviewer-14B | 14B | 🤗 link |
| Dataset Name | Train Data | Test Data | Description | HF Link |
|---|---|---|---|---|
| Review-5K | 4,189 | 781 | Peer review dataset for CycleReviewer training | 🤗 link |
| Research-14K | 12,696 | 802 | Research paper dataset for CycleResearcher training | 🤗 link |
| DeepReview-13K | 13,378 | 1,286 | Multi-perspective review dataset for DeepReviewer training | 🤗 link |
DeepReviewer offers three distinct review modes to accommodate different use cases:
Quick review generation for rapid feedback. Provides essential evaluation without multi-reviewer simulation.
Default mode that simulates multiple reviewers and includes self-verification to ensure reliable assessments.
Detect if content was generated by AI models:
from ai_researcher import AIDetector
# Initialize AI detector
detector = AIDetector(device='cpu')
# Analyze the generated paper
detection_result = detector.analyze_paper(paper)
print("Detection Results:")
print(f"Probability of AI generation: {detection_result['probability'] * 100:.2f}%")
print(f"Confidence Level: {detection_result['confidence_level']}")We have prepared comprehensive tutorials to help users understand and utilize our models:
- Tutorial 1: Getting Started with CycleResearcher 🚀
- Tutorial 2: Understanding CycleReviewer 📝
- Tutorial 3: Mastering DeepReviewer 🔍
- Tutorial 4: Creating an End-to-End Research Workflow 🔄
This code and the models' weights are provided under the CycleResearcher-License. See the LICENSE.md file for details.
If CycleResearcher is helpful to your work, please cite our paper:
@inproceedings{
weng2025cycleresearcher,
title={CycleResearcher: Improving Automated Research via Automated Review},
author={Yixuan Weng and Minjun Zhu and Guangsheng Bao and Hongbo Zhang and Jindong Wang and Yue Zhang and Linyi Yang},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=bjcsVLoHYs}
}if DeepReviewer is helpful to your work, please cite our paper:
@misc{zhu2025deepreviewimprovingllmbasedpaper,
title={DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process},
author={Minjun Zhu and Yixuan Weng and Linyi Yang and Yue Zhang},
year={2025},
eprint={2503.08569},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.08569},
}