tldrxiv

Inspiration

The world of academic research is vast and complex, and researchers often struggle to quickly understand papers outside their immediate expertise or to efficiently extract key insights from dense technical content. We created TLDRxiv after experiencing this frustration firsthand - wanting to explore research but finding the process time-consuming and mentally taxing. We envisioned a tool that could make research more accessible while maintaining the depth and rigor that makes academic work valuable.

What it does

TLDRxiv is an AI-powered research assistant that transforms how you interact with academic papers. It allows you to:

Chat with papers: Ask specific questions about any arXiv paper and receive clear, accurate responses with citations to external sources
Get concise summaries: Quickly understand the key contributions and findings without reading the entire paper
Explore citations and references: Discover related and understand the context and importance of citations
Generate audio versions: Convert papers to podcasts for learning on the go
Access properly formatted citations: Easily copy BibTeX citations for your own research

The platform uses Google's Gemini API with agentic capabilities to search the web for additional context when answering questions, providing transparent citations to external sources that enhance the reliability of responses.

How we built it

TLDRxiv is built with a modern, efficient tech stack:

Backend: FastAPI (Python) for a high-performance API server
Frontend: HTML, Tailwind CSS, and vanilla JavaScript for a responsive, clean interface
AI Integration: Google Gemini API for chat capabilities with grounding via Google Search. OpenAI API for text to speech
Data Sources: arXiv API for paper metadata and PDFs, Semantic Scholar API for citation data
Rendering: KaTeX for LaTeX equations, Marked.js for Markdown content

We implemented a streaming architecture that provides real-time responses while showing users exactly what sources the AI is using. The application is designed to be lightweight and accessible, requiring minimal dependencies while delivering a powerful research experience.

Challenges we ran into

Building TLDRxiv came with several significant challenges:

PDF processing: Scientific papers contain complex formatting, equations, and figures that are difficult to extract and process accurately
AI hallucinations: Ensuring that the AI provides factual information about papers without fabricating details
API limitations: Working within the constraints of various APIs, especially rate limits and response formats
Agentic capabilities: Implementing Google Search grounding in a way that enhances responses while maintaining transparency about sources
LaTeX rendering: Properly displaying mathematical equations in both the paper summaries and chat responses

Accomplishments that we're proud of

We're particularly proud of:

Creating a seamless chat experience that feels natural while providing accurate information about complex research
Building an elegant, responsive UI that makes academic research more accessible without sacrificing depth
Developing a system that handles LaTeX equations beautifully, maintaining the mathematical precision of the original papers
Crafting a solution that researchers can actually use in their daily workflow to enhance productivity

What we learned

This project taught us valuable lessons about:

The intricacies of working with large language models and their limitations when dealing with specialized knowledge
The importance of transparency in AI systems, especially when they're used for educational purposes
Techniques for streaming AI responses efficiently while processing and displaying them in real-time
Strategies for combining multiple APIs to create a cohesive, powerful application
The challenges of making complex technical content accessible without oversimplification

What's next for TLDRxiv

We have exciting plans to expand TLDRxiv's capabilities:

Custom research agents: Create specialized agents for a more compelling AI experience
Profile-based recommendations: Enhance our recommendations by providing feedback on papers

Try it out now at tldrxiv.org!