| --- |
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| title: Paper2Lab |
| emoji: 🧪 |
| colorFrom: purple |
| colorTo: blue |
| sdk: gradio |
| app_file: app.py |
| pinned: false |
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| tags: |
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| - backyard-ai |
| - scientific-research |
| - rag |
| - document-ai |
| - nvidia |
| - reproducibility |
| - gradio |
| - modal |
| - pdf |
| - llm |
|
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| --- |
| |
| # Paper2Lab |
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| Turn scientific papers into structured research artifacts, reproducibility assessments, and experiment-ready lab starter kits. |
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| ## Highlights |
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| - Tested on 40 scientific papers |
| - Supports Machine Learning, Clinical Research, Survey Studies, and Systematic Reviews |
| - Generates structured research artifacts in under 60 seconds |
| - Produces reproducibility assessments and experiment-ready lab starter kits |
| - Optional NVIDIA Nemotron refinement deployed on Modal |
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| ## Hackathon Submission |
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| ### Track |
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| 🏡 **Backyard AI** |
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| Paper2Lab was inspired by a conversation with a biology research student who struggled to move from reading scientific papers to actually reproducing their experiments. |
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| Researchers spend significant time extracting methodology details, identifying datasets, understanding evaluation protocols, and designing reproduction plans. |
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| Paper2Lab automates this workflow and transforms a paper into experiment-ready research artifacts in under 60 seconds. |
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| ## Why It Matters |
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| Researchers spend hours manually extracting datasets, methods, evaluation protocols, and reproducibility details from papers. |
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| Paper2Lab helps researchers move from reading papers to designing experiments by automatically generating structured summaries, evidence-grounded findings, reproducibility assessments, and lab starter kits. |
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| --- |
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| ## Live Demo |
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| **Hugging Face Space** |
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| https://huggingface.co/spaces/RLazreg/Paper2Lab |
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| --- |
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| ## Demo Video |
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| https://drive.google.com/file/d/1d1s7dcAjM_GdjeT4zhmqMPEH2Cxa4Sfb/view?usp=sharing |
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| Demo includes: |
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| - Attention Is All You Need |
| - Single-Cell RNA Sequencing Analysis |
| - NVIDIA Nemotron refinement |
| - Reproducibility assessment |
| - Lab starter kit generation |
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| --- |
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| ## Social Post |
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| LinkedIn: |
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| https://www.linkedin.com/feed/update/urn:li:ugcPost:7472403996360581120/ |
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| --- |
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| ## GitHub repository |
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| GitHub: |
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| https://github.com/miranitta/Paper2Lab |
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| --- |
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| ## Team |
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| Solo Submission |
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| Hugging Face Username: RLazreg |
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| --- |
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| ## What Paper2Lab Generates |
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| Upload a scientific paper and automatically obtain: |
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| * Structured Paper Card |
| * Evidence-Grounded Summary |
| * Dataset Extraction |
| * Model & Method Extraction |
| * Reproducibility Assessment |
| * Experiment Roadmap |
| * Lab Starter Kit |
| * Interactive Question Answering |
| * Exportable JSON Reports |
| * Exportable Markdown Reports |
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| --- |
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| ## Key Features |
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| ### Structured Paper Understanding |
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| Automatically extracts: |
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| * Research Question |
| * Contributions |
| * Methodology |
| * Datasets |
| * Models and Methods |
| * Metrics |
| * Findings |
| * Limitations |
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| ### Evidence Grounding |
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| Every extraction is linked to supporting evidence retrieved directly from the paper. |
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| ### Ask the Paper |
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| Ask questions such as: |
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| * What dataset was used? |
| * What model was proposed? |
| * What metrics were reported? |
| * What limitations were identified? |
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| ### Reproducibility Assessment |
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| Evaluates: |
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| * Dataset availability |
| * Experimental setup quality |
| * Hyperparameter reporting |
| * Evaluation completeness |
| * Code availability |
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| ### Lab Starter Kit |
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| Generates: |
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| * Project structure |
| * Required dependencies |
| * Dataset plan |
| * Experiment checklist |
| * Evaluation plan |
| * Reproducibility risks |
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| --- |
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| ## Technology Stack |
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| * Python |
| * Gradio |
| * PyMuPDF |
| * Sentence Transformers |
| * Local Semantic Search |
| * NVIDIA Nemotron |
| * Modal |
| * Hugging Face |
| |
| --- |
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| ## Evaluation |
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| Paper2Lab was tested on **40 scientific papers** spanning: |
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| * Machine Learning |
| * Clinical Research |
| * Survey Studies |
| * Systematic Reviews |
| * General Scientific Research |
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| Results: |
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| * End-to-end analysis in under 60 seconds |
| * Structured information extraction |
| * Reproducibility assessment |
| * Experiment-ready lab starter kits |
| * Evidence-grounded responses |
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| --- |
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| ## Architecture |
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| PDF |
| → PyMuPDF Extraction |
| → Evidence Indexing |
| → Structured Paper Card |
| → Reproducibility Assessment |
| → Lab Starter Kit |
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| By choice: |
| → NVIDIA Nemotron Refinement (via Modal) |
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| --- |
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| ## How It Works |
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| 1. Upload a PDF paper |
| 2. Extract paper content |
| 3. Build evidence index |
| 4. Generate structured paper card |
| 5. Optional NVIDIA Nemotron refinement |
| 6. Run reproducibility analysis |
| 7. Generate lab starter kit |
| 8. Export results |
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| --- |
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| ## Future Work |
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| * Multi-paper comparison |
| * Citation graph exploration |
| * Agentic research workflows |
| * Multi-document RAG |
| * Fine-tuned extraction models |
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| --- |
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| Built for researchers, students, engineers, and scientific teams who want to move from reading papers to running experiments. |
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