Research Paper Analyzer

AI-powered research assistant using NVIDIA NIM and agentic workflows.

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Inspiration

Academic researchers face a critical bottleneck: literature review. Studies show researchers spend nearly a quarter of their time manually searching, reading, and synthesizing papers. I witnessed this firsthand watching researchers struggle to keep up with the exponential growth of published papers in AI and ML. I wanted to build something that could act as a true research assistant - not just a search tool, but an autonomous agent that could think, plan, and synthesize information the way a human researcher does. With NVIDIA NIM's powerful reasoning capabilities and AWS's scalable infrastructure, I saw an opportunity to democratize access to academic literature and accelerate scientific discovery.

What it does

Research Paper Analyzer is an autonomous AI agent that conducts comprehensive literature reviews in minutes instead of days.

When given a research question, the agent:

  1. Autonomously decomposes complex questions into focused sub-queries
  2. Searches ArXiv for relevant academic papers
  3. Downloads and processes PDFs to extract text
  4. Performs multi-step reasoning across multiple papers
  5. Synthesizes findings with proper citations
  6. Compares research methodologies across sources
  7. Identifies gaps in current literature

Unlike traditional RAG systems that simply retrieve and summarize, this is a true agentic application that plans its own workflow, makes decisions, and produces original analysis. Every claim is backed by specific paper references, ensuring academic rigor.

How we built it

Tech Stack:

  • LLM: NVIDIA NIM (llama-3.1-nemotron-nano-8B-v1) for multi-step reasoning
  • Retrieval: ArXiv API for real-time paper search and download
  • Processing: PyPDF for text extraction from academic papers
  • Frontend: Streamlit for interactive UI
  • Deployment: AWS EC2 with systemd service management

Architecture: The core is an autonomous agent orchestrator that manages the entire workflow:

  • Query decomposition using LLM reasoning
  • Asynchronous paper retrieval from ArXiv
  • PDF text extraction and chunking
  • Context management for LLM synthesis
  • Citation tracking and validation

The agent uses chain-of-thought reasoning to break down complex queries, then executes a self-directed research loop. It's deployed on AWS EC2 using a user data bootstrap script for automated deployment, with the app running as a systemd service for reliability and auto-restart capabilities.

Key Innovation: True agentic behavior - the system plans, acts, reasons, and adapts autonomously without hardcoded workflows.

Challenges we ran into

  1. Python 3.13 Compatibility Initial deployment failed due to Python 3.13 removing the cgi module that dependencies relied on. Solution: Moved to Python 3.12 for better library support.

  2. Dependency Hell Windows compilation errors with grpcio and pymilvus for Python 3.13. We streamlined dependencies to only what was necessary, removing unused vector database components.

  3. AWS Deployment Complexity SSH connectivity issues made manual deployment challenging. Solution: Implemented automated deployment via EC2 User Data scripts, enabling zero-touch deployment.

  4. Context Window Management Academic papers are long. Balancing comprehensive analysis with LLM context limits required smart chunking and summarization strategies.

  5. Citation Accuracy Ensuring proper source attribution required careful prompt engineering and output parsing to maintain academic rigor.

The biggest lesson: Modern AI deployment requires thinking about the entire stack - from local development compatibility to production infrastructure automation.

Accomplishments that we're proud of

Built a truly autonomous agent - Not just a chatbot, but a system that plans, researches, and synthesizes independently

Achieved end-to-end functionality - From question to cited, multi-source analysis

Successfully deployed on AWS - Overcame significant deployment challenges to create a production-ready system

Real-world impact - Created something that genuinely saves researchers time and democratizes access to academic knowledge

Clean, modular architecture - Professional codebase that's maintainable and extensible

Proper academic rigor - Every claim is cited, maintaining research integrity

The system works exactly as envisioned: Ask a question, get a comprehensive, cited analysis synthesized from multiple papers in under 3 minutes. That's powerful.

What we learned

Technical Lessons:

  • Agentic AI requires careful orchestration - planning, execution, and synthesis must be coordinated
  • Prompt engineering is crucial for academic accuracy and citation tracking
  • Production deployment requires thinking beyond "it works on my machine"
  • Automated deployment (Infrastructure as Code) saves massive time and headaches

AI/ML Insights:

  • NVIDIA NIM's reasoning capabilities are impressive for complex multi-step tasks
  • Context management is critical when working with long documents
  • Chain-of-thought reasoning dramatically improves output quality
  • Agents need good error handling - papers can be malformed, downloads can fail

Soft Skills:

  • Persistence pays off - deployment failures are learning opportunities
  • Documentation is essential - future-me will thank present-me
  • User experience matters - making complex AI accessible requires thoughtful UI design

Biggest Takeaway: Building production AI systems is about much more than model selection. It's about the entire pipeline: data retrieval, processing, reasoning, presentation, and deployment. Every component matters.

What's next for Research Paper Analyzer

Short-term (Next Month):

  • Vector Database Integration: Add Milvus/NVIDIA NIM Embeddings for semantic search across paper corpus
  • Expanded Sources: Beyond ArXiv - add PubMed, IEEE Xplore, Google Scholar
  • Export Capabilities: Generate LaTeX bibliography, export to Markdown/Word
  • Batch Processing: Analyze multiple queries in parallel

Medium-term (3-6 Months):

  • Citation Graph Analysis: Visualize paper relationships and influence
  • Collaborative Features: Share analyses, comment, and annotate
  • Custom Fine-tuning: Domain-specific models for medicine, CS, physics, etc.
  • API Access: Let researchers integrate into their own workflows

Long-term Vision:

  • Research Copilot: Continuous monitoring of new papers in your field
  • Grant Writing Assistant: Use literature analysis to support proposals
  • Peer Review Helper: Compare submissions against existing literature
  • Multi-language Support: Break down language barriers in global research

Infrastructure:

  • Migrate to Kubernetes for better scalability
  • Add caching layer for frequently accessed papers
  • Implement rate limiting and user authentication
  • Add monitoring and analytics dashboard

The goal: Make this the go-to tool for every researcher, from grad students to senior professors, democratizing access to academic knowledge and accelerating scientific discovery globally.

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