🔬 ArXiv PaperLens: Intelligent Research Paper Discovery System

An AI-powered research assistant that learns your preferences and delivers personalized ArXiv paper recommendations

Overview

ArXiv PaperLens is a sophisticated RAG (Retrieval-Augmented Generation) system that revolutionizes how researchers discover and interact with academic papers. Using advanced machine learning techniques, it creates personalized research experiences by understanding user preferences through implicit and explicit feedback mechanisms.

Key Features

Intelligent Paper Discovery: Daily ArXiv crawling with ML-powered recommendations
Multimodal RAG Chat: Converse with papers using text and images via Google Gemini Pro
Advanced User Profiling: Dynamic embedding updates using exponential moving average algorithms
Personalized Recommendations: Adaptive scoring system based on user interaction patterns
Semantic Search: FAISS-powered vector similarity search across 100k+ papers
Multi-Channel Notifications: Email digests and Telegram bot integration
Modern UI: Responsive React frontend with real-time interactions

System Architecture

graph TB
    subgraph "Data Layer"
        A[ArXiv API] --> B[Daily Crawler Agent]
        B --> C[PDF Processing]
        C --> D[Text & Image Extraction]
        D --> E[Vector Database<br/>FAISS Index]
    end
    
    subgraph "AI Layer"
        F[Multimodal Embedder<br/>CLIP + BGE] --> G[User Embedding Service]
        G --> H[Recommendation Engine<br/>Exponential Moving Average]
        I[Gemini Pro RAG Agent] --> J[Multimodal Q&A]
    end
    
    subgraph "Application Layer"
        K[Backend] --> L[React Frontend]
        M[LangGraph Agents] --> N[Notification Service<br/>Email + Telegram]
    end
    
    E --> I
    E --> H
    G --> K
    H --> K
    J --> L
    N --> L

Mathematical Foundation

User Embedding Update Algorithm

The system employs an Exponential Moving Average (EMA) approach for updating user embeddings:

E_new = (1 - α) × E_current + α × E_weighted_papers

Where:

α: Learning rate (default: 0.1)
E_weighted_papers: Weighted average of paper embeddings based on interaction types
Interaction weights: Like(+1.0), Bookmark(+0.8), Share(+0.6), View(+0.1), Dislike(-0.5), Delete(-0.9)

Temporal Decay Mechanism

E_decayed = E × (decay_factor^(days_since_update/30))

This ensures recent preferences have higher influence while preventing embedding staleness.

Relevance Scoring

Paper relevance is computed using cosine similarity:

relevance = (cosine_similarity(E_user, E_paper) + 1) / 2

Normalized to [0,1] range for intuitive scoring.

Quick Start

Prerequisites

Python 3.9+
Node.js 16+ (for React frontend)
Google API Key (Gemini Pro)
PostgreSQL (for metadata storage)
Docker (optional deployment)

Installation

Clone and Setup

git clone https://github.com/aymen-000/PaperLens
cd Paperlans

# Install Python dependencies
pip install -r requirements.txt
# or using uv (recommended)
uv sync

Environment Configuration

# Create .env file
cat > .env << EOF
GOOGLE_API_KEY = "your_key"
CRAWLER_AGENT_MODEL_ID="gemini-2.5-flash"
PROVIDER = "langchain_google_genai.ChatGoogleGenerativeAI"
HUGGINGFACE_TOKEN_KEY = "your_token"
JWT_SECRET_KEY = "your_key"

Database Initialization

python scripts/init_db.py
python scripts/seed_user.py

Start Services

# Terminal 1: Backend API
source .venv/bin/activate
export PYTHONAPATH=.
python3 backend/app/app.py

# Terminal 2: Frontend (separate terminal)
npm install 
npm run dev

Access Application
- Frontend: http://localhost:3000
- Health Check: http://localhost:8000/health

Technology Stack

Backend

Flask: High-performance async web framework
LangChain: LLM orchestration and document processing
LangGraph: Agent workflow management
FAISS: Vector similarity search (Facebook AI)
PostgreSQL: Metadata and user data storage
SQLAlchemy: Database ORM

AI/ML Components

Google Gemini Pro: Multimodal language model
CLIP (OpenAI): Image-text understanding
BGE Embeddings: Semantic text embeddings
Sentence Transformers: Text encoding
PyTorch: Deep learning framework

Frontend

React: Modern UI framework
Tailwind CSS: Utility-first styling
Axios: HTTP client

Project Structure

Paperlens/
├── agents/                           #  AI Agent System
│   ├── system_agents/               # Core intelligent agents
│   │   ├── crawler.py              # ArXiv paper crawler
│   │   └── papers_rag.py           # Multimodal RAG system
│   ├── data/                       # Data processing & embeddings
│   │   ├── embedding.py            # User embedding service
│   │   ├── indexing.py             # Vector indexing
│   │   └── vector_db.py            # FAISS operations
│   ├── tools/                      # Agent utility tools
│   │   ├── crawler_tools.py        # PDF processing tools
│   │   └── rag_tools.py            # RAG helper functions
│   ├── prompts/                    # LLM prompt templates
│   └── config.py                   # Agent configurations
│
├── backend/                          #  FastAPI Application
│   └── app/
│       ├── models/                 # Database models
│       │   ├── user.py             # User profiles
│       │   ├── paper.py            # Paper metadata
│       │   ├── user_embedding.py   # User preference vectors
│       │   └── user_feedback.py    # Interaction tracking
│       ├── routes/                 # API endpoints
│       │   ├── papers_api.py       # Paper CRUD operations
│       │   ├── papers_bot.py       # RAG chat endpoints
│       │   └── user.py             # User management
│       ├── services/               # Business logic
│       │   ├── db_service.py       # Database operations
│       │   └── handle_interaction.py # User feedback processing
│       └── database.py             # SQLAlchemy setup
│
├── frontend/                         #  Next.js Application
│   ├── app/                        # Next.js App Router
│   │   ├── page.tsx                # Dashboard homepage
│   │   ├── login/                  # Authentication pages
│   │   └── signup/
│   ├── components/                 # UI components
│   │   ├── paper-feed.tsx          # Paper recommendation feed
│   │   ├── rag-panel.tsx           # Chat interface
│   │   ├── paper-search.tsx        # Search functionality
│   │   ├── settings-page.tsx       # User preferences
│   │   └── ui/                     # Shadcn/UI components
│   └── lib/                        # Utilities & API clients
│
├── faiss_index/                      #  Vector Database
│   ├── text_index.faiss            # Text embeddings (BGE)
│   ├── image_index.faiss           # Image embeddings (CLIP)
│   └── faiss_index/                # Legacy unified index
│
├── storage/                          #  File Storage
│   ├── raw/                        # Original PDF papers
│   ├── processed/                  # Extracted content
│   │   ├── images/                 # Figures & diagrams
│   │   └── paper_*/                # Per-paper text & images
│   └── papers/                     # Downloaded PDFs
│
└── scripts/                          #  Utility Scripts
    ├── init_db.py                  # Database initialization
    ├── seed_user.py                # Sample user creation
    └── run_agents.py               # Agent orchestration

Intelligent Agents

1. ArXiv Crawler Agent

Purpose: Automated daily paper discovery
Capabilities:
- Fetches 50+ papers daily based on user categories
- PDF text extraction using PyPDF2
- Figure/diagram extraction using PIL
- Metadata enrichment and storage

2. Multimodal RAG Agent

Purpose: Intelligent Q&A over research papers
Capabilities:
- Text-based semantic search
- Image understanding and analysis
- Context-aware response generation
- Source attribution and citation

3. User Learning Agent

Purpose: Preference modeling and adaptation
Capabilities:
- Real-time embedding updates
- Interaction pattern analysis
- Cold-start problem handling
- Temporal preference drift detection

4. Recommendation Engine

Purpose: Personalized content delivery
Capabilities:
- Multi-factor scoring algorithms
- Diversity-aware recommendations
- Category-based filtering
- Performance analytics

🔧 Advanced Configuration

Custom Categories and Weights

# User category preferences with weights
CATEGORY_WEIGHTS = {
    "cs.AI": 0.2,          # Artificial Intelligence
    "cs.LG": 0.2,          # Machine Learning  
    "cs.CV": 0.2,          # Computer Vision
    "stat.ML": 0.4,        # Statistics ML
}

Contributing

We welcome contributions!

Development Workflow

Fork the repository
Create feature branch (git checkout -b feature/amazing-feature)
Make changes with tests
Commit with conventional commits (feat:, fix:, docs:)
Push and create Pull Request

Areas for Contribution

New embedding models integration
Advanced analytics dashboards
Multi-language support
Performance optimizations
UI/UX improvements
User feedbacks
notification channels

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

ArXiv for providing open access to research papers
Google for Gemini Pro API access
Hugging Face for transformer models and embeddings
Facebook AI for FAISS vector search
OpenAI for CLIP multimodal understanding

Future Enhancements

Roadmap

Advanced Recommendation System

** Reinforcement Learning Recommender**
- Multi-Armed Bandit algorithms for exploration vs exploitation
- Deep Q-Network (DQN) for long-term user engagement optimization
- Contextual bandits considering user state, time, and reading patterns
- A/B testing framework for recommendation strategy evaluation

Enhanced User Feedback & Analytics

** Rich Feedback Mechanisms**
- Star ratings and detailed paper reviews
- Reading time tracking and attention heatmaps
- Bookmark organization with custom tags and collections
- Social features: following researchers, sharing reading lists
- Citation network analysis for impact-based recommendations
** Advanced Analytics Dashboard**
- Personal research journey visualization
- Topic evolution and trend analysis
- Collaboration opportunity detection
- Research gap identification using knowledge graphs

Multi-Source Content Aggregation

** Diversified Content Sources**
- LinkedIn Research Posts: Professional insights and industry research
- Twitter/X Academic Threads: Real-time research discussions and preprints
- Google Scholar: Citation networks and h-index tracking
- Research Gate: Social academic networking integration
- Medium/Towards Data Science: Practical implementations and tutorials
- GitHub Research Repos: Code implementations and reproducible research
** Social Media Intelligence**
- Tweet sentiment analysis for trending topics
- LinkedIn post engagement metrics
- Research influencer identification
- Conference hashtag monitoring (#NeurIPS2024, #ICML2024)

Phase 4: AI-Powered Research Assistant

** Advanced AI Capabilities**
- Literature gap analysis using LLMs
- Automated research proposal generation
- Cross-paper concept linking and knowledge graphs
- Research methodology recommendations
- Collaborative filtering with similar researchers
Research Workflow Integration
- Zotero/Mendeley synchronization
- LaTeX reference management
- Notion/Obsidian knowledge base integration
- Calendar integration for reading schedules
- Email digest with personalized research summaries

Support & Contact

Issues: GitHub Issues
Discussions: GitHub Discussions
Email: aymne011@gmail.com

Built with ❤️ for the research community

Making academic research discovery intelligent, personalized, and delightful

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Name		Name	Last commit message	Last commit date
Latest commit History 19 Commits
agents		agents
backend		backend
demo		demo
faiss_index		faiss_index
frontend		frontend
scripts		scripts
.gitignore		.gitignore
.python-version		.python-version
LICENSE		LICENSE
README.md		README.md
main.py		main.py
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
uv.lock		uv.lock

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🔬 ArXiv PaperLens: Intelligent Research Paper Discovery System

Overview

Key Features

System Architecture

Mathematical Foundation

User Embedding Update Algorithm

Temporal Decay Mechanism

Relevance Scoring

Quick Start

Prerequisites

Installation

Technology Stack

Backend

AI/ML Components

Frontend

Project Structure

Intelligent Agents

1. ArXiv Crawler Agent

2. Multimodal RAG Agent

3. User Learning Agent

4. Recommendation Engine

🔧 Advanced Configuration

Custom Categories and Weights

Contributing

Development Workflow

Areas for Contribution

License

Acknowledgments

Future Enhancements

Roadmap

Advanced Recommendation System

Enhanced User Feedback & Analytics

Multi-Source Content Aggregation

Phase 4: AI-Powered Research Assistant

Support & Contact

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