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