AI Research Lab

The tool aggregates recent arXiv papers from nine AI subfields and processes them through five specialized agents that identify contradictions, consensus, and emerging research frontiers. It filters for computer‑science work with abstracts, applies a citation‑based quality threshold, and enriches entries with metadata such as HuggingFace upvotes, Papers with Code links, and OpenReview embeddings. The resulting dataset is organized by topic, relevance, and citation count, and is refreshed weekly.

Researchers and developers can explore the curated collection via visual overviews of topic distribution, research intensity, and connections between papers. The system also provides embeddings (Gemini text‑embedding‑001, 768‑dimensional) and a similarity search index (cosine, HNSW) to support downstream analysis. All components are open source and free to use.

The project distinguishes itself by combining automated LLM‑driven agents with a structured, citation‑aware pipeline, delivering a searchable, continuously updated snapshot of influential AI literature without requiring manual reading of each paper.