Amitech

ArXplain: Research papers, explained at your level It reads a research paper, finds out what it assumes you already know, asks what you actually know and then explains every section of the paper at exactly your level. The problem: Research papers are written for experts. A student reading "Attention Is All You Need" hits scaled dot-product attention, BLEU scores, ablations, and unstated assumptions all at once with no way to know which gaps matter or where to fill them. Generic AI summaries don't help: they flatten every paper to the same depth for every reader. What ArXplain does: It's a CLI-based multi-step agentic system with two phases: 1. Profiling: An LLM, assisted by web search (Tavily/DDGS), analyses the paper and classifies its topics as Hard Requirements or Good To Know. For each topic, you're shown a passage from the paper itself plus a plain-language self-check question, and you rate your familiarity from 1–10. Your answers are saved as a per-paper profile.json. 2. Explanation: ArXplain explains every section of the actual paper: Abstract, Background, Experiments, Results, Conclusion, References in the paper's own order, adapted to your ratings: slowed down with basics where you rated low, brief recap where you rated high. Each section lists its hard requirements; figures appear in the sections that discuss them, references link to the actual cited papers, and every external learning resource lives exactly once in a Resources sidebar that section topics jump-link to. Why it's different: The explanation is personalised per topic, not per user stereotype; it explains the real paper instead of summarising it; and the LLM pipeline is cost-engineered with one batched call per explanation, hard call caps, and disk caching, so a full paper costs roughly 1,700 tokens. Built with: Python, Meta Model API (Muse Spark), Tavily/DDGS search, arXiv full-text ingestion with a canonical versioned JSON paper schema.