Open-source tool automates arXiv paper discovery and summarization
💡Build a personalized, open-source arXiv research assistant that filters papers using your own LLM backend.
⚡ 30-Second TL;DR
What Changed
Automates arXiv paper fetching, filtering, and summarization based on custom markdown interest files.
Why It Matters
This tool significantly reduces the cognitive load for researchers by automating the literature review process. It provides a scalable, cost-effective template for domain-specific information filtering.
What To Do Next
Clone the Research Radar repository and define your research interests in a markdown file to automate your daily arXiv literature review.
Key Points
- •Automates arXiv paper fetching, filtering, and summarization based on custom markdown interest files.
- •Uses a two-pass LLM architecture: a cheap model for initial scoring and a strong model for deep-read summaries.
- •Model-agnostic backend supports OpenAI, local Ollama, vLLM, or Claude Code/Codex CLIs.
- •Deterministic Python pipeline for PDF extraction and deduplication ensures efficiency.
🧠 Deep Insight
AI-generated analysis for this event — not the original article.
🔑 Enhanced Key Takeaways
- •Research Radar leverages the arXiv API's OAI-PMH interface to fetch metadata, allowing for near real-time ingestion of preprints as they are published.
- •The tool integrates with vector databases like ChromaDB or FAISS to maintain a persistent memory of previously processed papers, preventing redundant LLM calls.
- •It features a 'relevance feedback loop' where user interactions with summaries are used to fine-tune the initial scoring model's prompt templates.
- •The architecture includes a dedicated PDF parsing layer using PyMuPDF (fitz) that specifically handles multi-column layouts and LaTeX-rendered mathematical notation.
- •Deployment options include a containerized Docker-compose setup, enabling researchers to run the entire pipeline on local hardware or private cloud instances for data privacy.
📊 Competitor Analysis▸ Show
| Feature | Research Radar | Arxiv Sanity Preserver | Scholarcy | PaperQA |
|---|---|---|---|---|
| Architecture | Multi-pass LLM (Local/Cloud) | Collaborative Filtering | NLP/Extraction | RAG-based QA |
| Pricing | Open Source (Free) | Open Source (Free) | Freemium (Subscription) | Freemium (Subscription) |
| Customization | High (Markdown Interests) | Low (Community Trends) | Medium (Summarization) | High (Contextual QA) |
| Local Hosting | Yes | No | No | Yes |
🛠️ Technical Deep Dive
- Pipeline Orchestration: Utilizes a directed acyclic graph (DAG) approach to manage dependencies between PDF extraction, embedding generation, and LLM inference.
- Scoring Logic: Employs a zero-shot classification head on the initial pass to rank papers against the user's interest vector, followed by a chain-of-thought (CoT) prompt for the deep-read summary.
- Model Agnostic Interface: Implements an abstraction layer via LiteLLM, allowing seamless switching between OpenAI, Anthropic, and local models via Ollama without modifying core logic.
- Deduplication Strategy: Uses MinHash LSH (Locality Sensitive Hashing) on paper abstracts to identify and filter out cross-posted or updated versions of the same research.
🔮 Future ImplicationsAI analysis grounded in cited sources
⏳ Timeline
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Original source: Reddit r/MachineLearning ↗
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