Datasets:

ItsMaxNorm
/

pc-database

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

	> Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework
	>
	> Komal Kumar<sup>1</sup>, Aman Chadha<sup>2</sup>, Salman Khan<sup>1</sup>, Fahad Shahbaz Khan<sup>1</sup>, Hisham Cholakkal<sup>1</sup>
	>
	> <sup>1</sup> Mohamed bin Zayed University of Artificial Intelligence   <sup>2</sup> 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)