{"repo":"ansumandas441/mathematical-discovery-engine","free":true,"listed":false,"github":"https://github.com/ansumandas441/mathematical-discovery-engine","clone":"git clone https://github.com/ansumandas441/mathematical-discovery-engine.git","description":"This is a mathematical discovery engine, which searches new mathematics applying techniques to existing results","language":"Python","stars":37,"topics":["knowledge-graph","mathematics","artificial-intelligence","automated-reasoning","discovery-engine","graph-theory","knowledge-base","llm","machine-learning","mathematical-engine"],"license":"MIT","category":"ai-agents","readme_excerpt":"Mathematical Discovery Engine (MDE) Treat mathematical proof as path-finding through a graph. A knowledge graph of 15,941 mathematical states and 1,223 proof techniques — from Pythagoras to Perelman — that an LLM searches to discover new theorems. ▶ Explore the live interactive 3D graph →  ·  no install, runs in your browser 15,941 nodes · 26,614 edges · 1,223 proof techniques · 5,055 theorems — click the graph to explore it in 3D What This Repository Does This repository encodes the structure of mathematical knowledge as a graph and uses it to discover new theorems automatically . It starts from a simple observation: every major theorem in mathematics was discovered by applying known techniques to known facts. Galois used symmetry reduction on polynomial roots. Cantor used diagonalization on real numbers. Wiles used a bridge between modular forms and elliptic curves. The techniques are reusable — the same method of proof appears across centuries and subdisciplines. This project does three things: 1. Collects the knowledge. A detailed report covering 100 of the most consequential theorems in mathematics — from Pythagoras through Perelman — with the discovery context, motivation, thought process, and proof ideas behind each. The report spans 13 chapters organized chronologically, drawing from primary sources (Euler's letters, Gauss's diaries, Ramanujan's notebooks, Wiles's interviews) where they survive. 2. Builds a knowledge graph. The report is distilled into a str","default_branch":null,"files":null,"tree":[],"storefront":"/r/ansumandas441","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/ansumandas441/mathematical-discovery-engine/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}