Open-source tool automates arXiv paper discovery and summarization

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🤖Read original on Reddit r/MachineLearning

#arxiv #automation #llm-agents #open-sourceresearch-radararxiv ollama vllm claude openai

💡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.

Who should care:Researchers & Academics

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.

🔑 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

Personalized research agents will reduce literature review time by over 70%.

Automated filtering and summarization allow researchers to bypass manual browsing, focusing only on high-relevance content identified by LLMs.

Open-source research tools will shift the arXiv ecosystem toward decentralized discovery.

As researchers move away from centralized platforms like Twitter or Google Scholar for discovery, they will rely more on private, interest-aligned pipelines.