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ItsMaxNorm
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pc-research

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The dataset viewer is not available for this split.

Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
links: struct<papers: list<item: struct<title: string, url: string, pdf_url: string, doi_url: string, source: string, year: int64>>, by_source: struct<offline_iclr: list<item: struct<title: string, url: string, pdf_url: string, doi_url: null, source: string, year: int64>>, offline_nips: list<item: struct<title: string, url: string, pdf_url: string, doi_url: null, source: string, year: int64>>, offline_icml: list<item: struct<title: string, url: string, pdf_url: string, doi_url: null, source: string, year: int64>>, offline_cvpr: list<item: struct<title: string, url: string, pdf_url: string, doi_url: null, source: string, year: int64>>, semantic_scholar: list<item: struct<title: string, url: string, pdf_url: string, doi_url: string, source: string, year: int64>>, arxiv: list<item: struct<title: string, url: string, pdf_url: string, doi_url: null, source: string, year: int64>>>, pdfs_only: list<item: struct<title: string, pdf: string>>, dois_only: list<item: struct<title: string, doi: string>>>
metadata: struct<query: string, total_steps: int64, last_updated: string, started_at: string, total_papers: int64>
vs
papers: list<item: struct<title: string, authors: list<item: string>, abstract: string, url: string, year: int64, venue: string, source: string, doi: string, pdf_url: string, citations: int64, categories: list<item: string>, id: string, track: string, status: string, keywords: string, tldr: string, primary_area: string, similarity_score: double, novelty_score: double, recency_score: double, relevance_score: double, bm25_score: double, combined_score: double, rank: int64>>
metadata: struct<query: string, total_steps: int64, last_updated: string, started_at: string, total_papers: int64>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 572, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              links: struct<papers: list<item: struct<title: string, url: string, pdf_url: string, doi_url: string, source: string, year: int64>>, by_source: struct<offline_iclr: list<item: struct<title: string, url: string, pdf_url: string, doi_url: null, source: string, year: int64>>, offline_nips: list<item: struct<title: string, url: string, pdf_url: string, doi_url: null, source: string, year: int64>>, offline_icml: list<item: struct<title: string, url: string, pdf_url: string, doi_url: null, source: string, year: int64>>, offline_cvpr: list<item: struct<title: string, url: string, pdf_url: string, doi_url: null, source: string, year: int64>>, semantic_scholar: list<item: struct<title: string, url: string, pdf_url: string, doi_url: string, source: string, year: int64>>, arxiv: list<item: struct<title: string, url: string, pdf_url: string, doi_url: null, source: string, year: int64>>>, pdfs_only: list<item: struct<title: string, pdf: string>>, dois_only: list<item: struct<title: string, doi: string>>>
              metadata: struct<query: string, total_steps: int64, last_updated: string, started_at: string, total_papers: int64>
              vs
              papers: list<item: struct<title: string, authors: list<item: string>, abstract: string, url: string, year: int64, venue: string, source: string, doi: string, pdf_url: string, citations: int64, categories: list<item: string>, id: string, track: string, status: string, keywords: string, tldr: string, primary_area: string, similarity_score: double, novelty_score: double, recency_score: double, relevance_score: double, bm25_score: double, combined_score: double, rank: int64>>
              metadata: struct<query: string, total_steps: int64, last_updated: string, started_at: string, total_papers: int64>

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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] [arXiv] [Live Demo] [Benchmark]

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
Papers API	Space	ItsMaxNorm/papercircle-papers-api
Benchmark Leaderboard	Space	ItsMaxNorm/pc-bench
Benchmark Results	Dataset	ItsMaxNorm/pc-benchmark
Research Sessions	Dataset	ItsMaxNorm/pc-research

Getting Started

Prerequisites

Node.js >= 18 and Python >= 3.10
A Supabase project
An LLM provider: Ollama (local), OpenAI, or Anthropic

Install and Run

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 for detailed setup and 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
Retrieval	Recall@k, MRR, Success Rate	30+ conferences	docs/BENCHMARKS.md

# 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 Interactive leaderboard: ItsMaxNorm/pc-bench

Citation

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

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

Acknowledgments

arXiv • Supabase • smolagents • LiteLLM • Ollama • Hugging Face

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Paper for ItsMaxNorm/pc-research

Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework

Paper • 2604.06170 • Published 3 days ago • 20