<?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>Publications on AIM Blog</title><link>https://ai-mathematician.net/en/publications/</link><description>Recent content in Publications on AIM Blog</description><generator>Hugo</generator><language>en</language><atom:link href="https://ai-mathematician.net/en/publications/index.xml" rel="self" type="application/rss+xml"/><item><title>AI Mathematician: Towards Fully Automated Frontier Mathematical Research</title><link>https://arxiv.org/abs/2505.22451</link><pubDate>Wed, 28 May 2025 00:00:00 +0000</pubDate><guid>https://arxiv.org/abs/2505.22451</guid><category>Publications</category><description>Introduces AIM, a large-reasoning-model agent framework for frontier mathematical research, combining longer exploration trajectories with verification-oriented mechanisms for research-level proof tasks.</description></item><item><title>From Meta Idea to Advanced Mathematical Discovery -- Human-AI Co-Discovery of Sign-Embedding Quantum Algorithms</title><link>https://arxiv.org/abs/2606.24899</link><pubDate>Fri, 12 Jun 2026 00:00:00 +0000</pubDate><guid>https://arxiv.org/abs/2606.24899</guid><category>Publications</category><description>Presents a human-AI co-discovery case study in which AIM-supported exploration, theorem formation, derivation, and audit workflows contributed to sign-embedding quantum algorithms for matrix equations and matrix functions.</description></item><item><title>Pessimistic Verification for Open Ended Math Questions</title><link>https://arxiv.org/abs/2511.21522</link><pubDate>Wed, 26 Nov 2025 00:00:00 +0000</pubDate><guid>https://arxiv.org/abs/2511.21522</guid><category>Publications</category><description>Studies pessimistic verification workflows for open-ended mathematical proofs, where a proof is rejected when any verifier finds a critical error.</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><pubDate>Thu, 30 Oct 2025 00:00:00 +0000</pubDate><guid>https://arxiv.org/abs/2510.26380</guid><category>Publications</category><description>Presents a homogenization-theory case study showing how AIM-assisted reasoning and targeted human intervention can support the development of a complete mathematical proof.</description></item><item><title>FormaRL: Enhancing Autoformalization with no Labeled Data</title><link>https://arxiv.org/abs/2508.18914</link><pubDate>Tue, 26 Aug 2025 00:00:00 +0000</pubDate><guid>https://arxiv.org/abs/2508.18914</guid><category>Publications</category><description>Proposes FormaRL, a reinforcement-learning framework for autoformalization that uses unlabeled data, Lean syntax checks, and LLM consistency checks to improve formalization accuracy.</description></item></channel></rss>