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[2602.13695] Can a Lightweight Automated AI Pipeline Solve Research-Level Mathematical Problems?
Summary
This article explores the potential of a lightweight AI pipeline to solve complex mathematical problems, demonstrating its effectiveness with novel datasets and rigorous verification.
Why It Matters
The research highlights advancements in AI's ability to tackle sophisticated mathematical challenges, showcasing the integration of large language models in research applications. This has implications for both the fields of mathematics and artificial intelligence, potentially streamlining problem-solving processes in academia and beyond.
Key Takeaways
- Next-generation AI models can effectively solve research-level mathematical problems.
- The proposed pipeline integrates citation-based verification for enhanced accuracy.
- Results include verified proofs for complex mathematical problems, contributing to the field of AI for mathematics.
Computer Science > Artificial Intelligence arXiv:2602.13695 (cs) [Submitted on 14 Feb 2026] Title:Can a Lightweight Automated AI Pipeline Solve Research-Level Mathematical Problems? Authors:Lve Meng (University of Science and Technology of China, Zhongguancun Academy), Weilong Zhao (Université Paris Cité), Yanzhi Zhang (Zhongguancun Academy), Haoxiang Guan (Zhongguancun Academy), Jiyan He (Zhongguancun Academy) View a PDF of the paper titled Can a Lightweight Automated AI Pipeline Solve Research-Level Mathematical Problems?, by Lve Meng (University of Science and Technology of China and 5 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) have recently achieved remarkable success in generating rigorous mathematical proofs, with "AI for Math" emerging as a vibrant field of research. While these models have mastered competition-level benchmarks like the International Mathematical Olympiad and show promise in research applications through auto-formalization, their deployment via lightweight, natural-language pipelines for research problems remains underexplored. In this work, we demonstrate that next-generation models (e.g., Gemini 3 Pro, GPT-5.2 Pro), when integrated into a streamlined automated pipeline optimized for citation-based verification, can solve sophisticated research-grade problems. We evaluate our pipeline on two novel datasets: (1) the ICCM problem sets (comparable to the S.-T. Yau College Student Mathematics Contest) proposed by l...