ItsMaxNorm/pc-database

---
license: mit
---
---

> **Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework**
>
> Komal Kumar¹, Aman Chadha², Salman Khan¹, Fahad Shahbaz Khan¹, Hisham Cholakkal¹
>
> ¹ Mohamed bin Zayed University of Artificial Intelligence   ² AWS Generative AI Innovation Center, Amazon Web Services
>
> [[Github]](https://github.com/MAXNORM8650/papercircle)   [[arXiv]](https://arxiv.org/abs/2604.06170)   [[Live Demo]](https://papercircle.vercel.app)   [[Benchmark]](https://huggingface.co/spaces/ItsMaxNorm/pc-bench)

## Features

- **Paper Discovery** — Multi-agent AI search across arXiv, Scopus, and IEEE with hybrid BM25 + TF-IDF ranking and three discovery modes (Stable, Discovery, Balanced)
- **Paper Mind Graph** — LLM-powered extraction of concepts, methods, and experiments into structured knowledge graphs with interactive Q&A
- **Paper Review Generation** — Conference-format reviews (ICLR/NeurIPS/ICML style) via multi-agent analysis with lineage extraction
- **Paper Lineage** — Relationship mapping (extends/applies/evaluates/contradicts/survey/prerequisite) with interactive graph visualization
- **Reading Circles** — Community-based reading groups with role-based access, session scheduling, RSVP, and discussion threads

---

## Hugging Face Resources

| Resource | Type | Link |
|----------|------|------|
| **Papers Database** | Dataset | [ItsMaxNorm/pc-database](https://huggingface.co/datasets/ItsMaxNorm/pc-database) |
| **Papers API** | Space | [ItsMaxNorm/papercircle-papers-api](https://huggingface.co/spaces/ItsMaxNorm/papercircle-papers-api) |
| **Benchmark Leaderboard** | Space | [ItsMaxNorm/pc-bench](https://huggingface.co/spaces/ItsMaxNorm/pc-bench) |
| **Benchmark Results** | Dataset | [ItsMaxNorm/pc-benchmark](https://huggingface.co/datasets/ItsMaxNorm/pc-benchmark) |
| **Research Sessions** | Dataset | [ItsMaxNorm/pc-research](https://huggingface.co/datasets/ItsMaxNorm/pc-research) |

---

## Getting Started

### Prerequisites

- **Node.js** >= 18 and **Python** >= 3.10
- A [Supabase](https://supabase.com) project
- An LLM provider: [Ollama](https://ollama.com) (local), OpenAI, or Anthropic

### Install and Run

```bash
git clone https://github.com/MAXNORM8650/papercircle.git
cd papercircle

# Install
npm install
pip install -r backend/requirements-prod.txt

# Configure
cp .env.example .env # Edit with your Supabase & LLM credentials

# Run
npm run dev # Frontend (localhost:5173)
python backend/apis/fast_discovery_api.py # Discovery API (localhost:8000)
python backend/apis/paper_review_server.py # Review API (localhost:8005)
python backend/apis/paper_analysis_api.py # Analysis API (localhost:8006)
```

See [docs/QUICK_START.md](docs/QUICK_START.md) for detailed setup and [docs/DEPLOYMENT_GUIDE.md](docs/DEPLOYMENT_GUIDE.md) for production deployment.

---

## Project Structure

```
papercircle/
├── src/ # Frontend (React 18 + TypeScript)
│ ├── components/
│ │ ├── Papers/ # Paper discovery, detail, analysis views
│ │ ├── Lineage/ # Paper relationship graph & analysis hub
│ │ ├── Sessions/ # Session scheduling, RSVP, attendance
│ │ ├── Communities/ # Reading circle management
│ │ ├── Dashboard/ # User dashboard
│ │ ├── Auth/ # Authentication modals
│ │ ├── Layout/ # Header, navigation
│ │ ├── Admin/ # Admin panel
│ │ └── Settings/ # LLM & user settings
│ ├── contexts/ # AuthContext, CommunityContext, LineageAnalysisContext
│ ├── lib/ # Supabase client, API helpers, arXiv client
│ └── hooks/ # Custom React hooks
│
├── backend/
│ ├── agents/
│ │ ├── paper_review_agents/ # Multi-agent review generation & benchmarking
│ │ │ ├── orchestrator.py # Agent orchestration pipeline
│ │ │ ├── specialized_agents.py # Critic, Literature, Reproducibility agents
│ │ │ ├── benchmark_framework.py # Review benchmark framework
│ │ │ ├── benchmark_paper_review.py # Benchmark CLI
│ │ │ ├── evaluation_metrics.py # MSE, MAE, correlation, accuracy metrics
│ │ │ └── benchmark_results/ # Cached benchmark outputs
│ │ ├── paper_mind_graph/ # Knowledge graph extraction from PDFs
│ │ │ ├── graph_builder.py # LLM-based concept/method extraction
│ │ │ ├── qa_system.py # Interactive Q&A over papers
│ │ │ ├── ingestion.py # PDF parsing & chunking
│ │ │ └── export.py # JSON/Markdown/Mermaid/HTML export
│ │ ├── discovery/ # Paper discovery agents & ranking
│ │ └── agents/ # Core query & research agents
│ ├── apis/
│ │ ├── fast_discovery_api.py # Discovery API (port 8000)
│ │ ├── paper_review_server.py # Review API (port 8005)
│ │ ├── paper_analysis_api.py # Analysis API (port 8006)
│ │ ├── community_papers_api.py # Community papers API
│ │ ├── research_pipeline_api.py # Research pipeline API
│ │ └── unified/ # Unified Docker API (app.py + routers/)
│ ├── core/ # paperfinder.py, discovery_papers.py
│ ├── services/ # HuggingFace papers client
│ └── utils/ # Storage utilities
│
├── supabase/
│ ├── migrations/ # 55 SQL migrations (schema, RLS, seeds)
│ └── functions/ # Edge functions (arxiv-search)
│
├── api/ # Vercel serverless functions
│ ├── arxiv.js # arXiv CORS proxy
│ ├── community-papers.js # Community papers endpoint
│ └── sync-status.js # Sync status endpoint
│
├── scripts/ # Utility scripts
│ ├── javascript/ # arxiv-proxy, search engine, test scripts
│ ├── shell/ # Start scripts for each API service
│ └── *.py # Dataset builder, sync, DB fixes
│
├── docs/ # Documentation
│ ├── BENCHMARKS.md # Benchmark guide (review + retrieval)
│ ├── QUICK_START.md # Quick start guide
│ ├── DEPLOYMENT_GUIDE.md # Production deployment
│ ├── SECURITY.md # Security guidelines
│ ├── MIGRATION_COMPLETE.md # Serverless migration summary
│ └── PAPER_REVIEW_AGENTS_IMPLEMENTATION.md # Review system implementation
│
├── examples/
│ ├── pc-data/ # Benchmark datasets
│ └── docs/ # Architecture & integration guides
│ ├── ARCHITECTURE_DIAGRAMS.md # System diagrams
│ ├── MULTI_AGENT_PIPELINE_ARCHITECTURE.md
│ ├── ORCHESTRATOR_ARCHITECTURE.md
│ ├── PAPER_MIND_GRAPH_ARCHITECTURE.md
│ ├── AGENT_OPTIMIZATION_GUIDE.md
│ ├── RERANKER_INTEGRATION_SUMMARY.md
│ └── setup/ # Module setup & integration guides
│
├── hf_spaces/ # HuggingFace Spaces (Papers API app)
├── assets/ # Architecture & results figures
└── public/ # Logo and static assets
```

---

## Benchmarks

Two evaluation suites: **Review Quality** (AI reviews vs human reviewers) and **Retrieval Quality** (paper search accuracy).

| Benchmark | Metrics | Conferences | Details |
|-----------|---------|-------------|---------|
| **Paper Review** | MSE, MAE, Pearson r, Spearman ρ, Accuracy ±0.5/1.0/1.5 | ICLR, NeurIPS, ICML | [docs/BENCHMARKS.md](docs/BENCHMARKS.md) |
| **Retrieval** | Recall@k, MRR, Success Rate | 30+ conferences | [docs/BENCHMARKS.md](docs/BENCHMARKS.md) |

```bash
# Review benchmark
python backend/agents/paper_review_agents/benchmark_paper_review.py \
--data iclr2024.json --conference iclr --limit 100

# Retrieval benchmark
python benchmark_multiagent.py --queries queries.json --baseline bm25+reranker
```

Model results: [ItsMaxNorm/pc-benchmark](https://huggingface.co/datasets/ItsMaxNorm/pc-benchmark)   Interactive leaderboard: [ItsMaxNorm/pc-bench](https://huggingface.co/spaces/ItsMaxNorm/pc-bench)

---

## Citation

If you find PaperCircle useful in your research, please cite our paper:

```bibtex
misc{kumar2026papercircleopensourcemultiagent,
title={Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework},
author={Komal Kumar and Aman Chadha and Salman Khan and Fahad Shahbaz Khan and Hisham Cholakkal},
year={2026},
eprint={2604.06170},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2604.06170},
}
```

---

## License

MIT License — see [LICENSE](LICENSE)

## Acknowledgments

[arXiv](https://arxiv.org) • [Supabase](https://supabase.com) • [smolagents](https://github.com/huggingface/smolagents) • [LiteLLM](https://github.com/BerriAI/litellm) • [Ollama](https://ollama.com) • [Hugging Face](https://huggingface.co)