<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Discoveries on AIM Blog</title><link>https://ai-mathematician.net/en/discoveries/</link><description>Recent content in Discoveries on AIM Blog</description><generator>Hugo</generator><language>en</language><atom:link href="https://ai-mathematician.net/en/discoveries/index.xml" rel="self" type="application/rss+xml"/><item><title>Sign Embedding Quantum Algorithms for Matrix Equations and Matrix Functions</title><link>https://arxiv.org/abs/2604.25333</link><guid>https://arxiv.org/abs/2604.25333</guid><category>Discoveries</category><description>This work explores quantum algorithms for difficult matrix computations that appear in scientific computing and applied mathematics. Its main contribution is a reusable framework that may help quantum computers solve several families of matrix problems more systematically.</description></item><item><title>AI Mathematician as a Partner in Advancing Mathematical Discovery -- A Case Study in Homogenization Theory</title><link>https://arxiv.org/abs/2510.26380</link><guid>https://arxiv.org/abs/2510.26380</guid><category>Discoveries</category><description>This case study shows how AIM can support mathematicians on a long and technical proof. AIM helps propose reasoning steps and organize the search, while human researchers guide, check, and refine the proof to maintain mathematical rigor.</description></item></channel></rss>