Can a Lightweight Automated AI Pipeline Solve Research-Level Mathematical Problems?

2026-02-14

AI Summary

This work investigates whether lightweight natural language AI pipelines can solve research-level mathematical problems without formal verification environments. To this end, we develop an automated reasoning framework that integrates state-of-the-art large language models—such as Gemini 3 Pro and GPT-5.2 Pro—with a citation-driven verification mechanism. For the first time, this approach generates and validates multiple previously unpublished research-level mathematical proofs entirely within a natural language setting. Experiments on the ICCM benchmark and a newly introduced “First Proof” dataset demonstrate that our method successfully produces candidate proofs for all test problems. Notably, the solutions to the first two ICCM problem sets and Problem 4 from the “First Proof” dataset have been manually verified, with results submitted and publicly released.

Technology Category

• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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 leading mathematicians, and (2) the"First Proof"problem set, consisting of previously unpublished research questions. Our pipeline generated candidate proofs for all problems in the first two ICCM sets and the"First Proof"set. The solutions for the first two ICCM sets and Problem 4 of the"First Proof"set have been fully verified by our team. All generated proofs have been submitted to the official organization, and our generated results are publicly available. We plan to open-source the complete pipeline methodology in due course.

Problem

• lightweight AI pipeline
• research-level mathematical problems
• automated proof generation
• large language models
• mathematical reasoning

Innovation

• lightweight AI pipeline
• research-level mathematical problems
• automated proof generation
• citation-based verification
• large language models