# Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics

arXiv: https://arxiv.org/abs/2307.02502 · HTML: https://mlsanity.com/paper/2307.02502/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Placed in

- LLM agents and tool use / [Educational agents and math problem solving](https://mlsanity.com/agents/educational-agents-and-math-problem-solving/)

## Builds on (most cited first, 6 of 6)

- [LeanDojo: Theorem Proving with Retrieval-Augmented Language Models](https://mlsanity.com/paper/2306.15626/) (2023)
- [HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution](https://mlsanity.com/paper/2306.15794/) (2023)
- [The Unreasonable Effectiveness of Deep Learning in Artificial Intelligence](https://mlsanity.com/paper/2002.04806/) (2020)
- [A formal proof of the Kepler conjecture](https://mlsanity.com/paper/1501.02155/) (2015)
- [WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings](https://mlsanity.com/paper/2306.09328/) (2023)
- [Document Embedding for Scientific Articles: Efficacy of Word Embeddings vs TFIDF](https://mlsanity.com/paper/2107.05151/) (2021)

## Built on by (most cited first, 15 of 15)

- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [MasRouter: Learning to Route LLMs for Multi-Agent Systems](https://mlsanity.com/paper/2502.11133/) (2025)
- [Lifelong Learning of Large Language Model based Agents: A Roadmap](https://mlsanity.com/paper/2501.07278/) (2025)
- [LLM Agents for Education: Advances and Applications](https://mlsanity.com/paper/2503.11733/) (2025)
- [TravelAgent: An AI Assistant for Personalized Travel Planning](https://mlsanity.com/paper/2409.08069/) (2024)
- [Do LLM Agents Have Regret? A Case Study in Online Learning and Games](https://mlsanity.com/paper/2403.16843/) (2024)
- [Transforming Wearable Data into Personal Health Insights using Large Language Model Agents](https://mlsanity.com/paper/2406.06464/) (2024)
- [Sibyl: Simple yet Effective Agent Framework for Complex Real-world Reasoning](https://mlsanity.com/paper/2407.10718/) (2024)
- [Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with LLM-driven AI Agents in a Real-time Shared Workspace Task](https://mlsanity.com/paper/2409.08811/) (2024)
- [CCA: Collaborative Competitive Agents for Image Editing](https://mlsanity.com/paper/2401.13011/) (2024)
- [Causal Agent based on Large Language Model](https://mlsanity.com/paper/2408.06849/) (2024)
- [Orchestrating Intelligence: Confidence-Aware Routing for Efficient Multi-Agent Collaboration across Multi-Scale Models](https://mlsanity.com/paper/2601.04861/) (2026)
- [Teaching According to Students' Aptitude: Personalized Mathematics Tutoring via Persona-, Memory-, and Forgetting-Aware LLMs](https://mlsanity.com/paper/2511.15163/) (2025)
- [TestAgent: An Adaptive and Intelligent Expert for Human Assessment](https://mlsanity.com/paper/2506.03032/) (2025)

## Abstract (arXiv)

The advancement in generative AI could be boosted with more accessible mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in programming, algorithm discovery, and theorem proving, yet their genomics application is limited. This project introduces Math Agents and mathematical embedding as fresh entries to the "Moore's Law of Mathematics", using a GPT-based workflow to convert equations from literature into LaTeX and Python formats. While many digital equation representations exist, there's a lack of automated large-scale evaluation tools. LLMs are pivotal as linguistic user interfaces, providing natural language access for human-AI chat and formal languages for large-scale AI-assisted computational infrastructure. Given the infinite formal possibility spaces, Math Agents, which interact with math, could potentially shift us from "big data" to "big math". Math, unlike the more flexible natural language, has properties subject to proof, enabling its use beyond traditional applications like high-validation math-certified icons for AI alignment aims. This project aims to use Math Agents and mathematical embeddings to address the ageing issue in information systems biology by applying multiscalar physics mathematics to disease models and genomic data. Generative AI with episodic memory could help analyse causal relations in longitudinal health records, using SIR Precision Health models. Genomic data is suggested for addressing the unsolved Alzheimer's disease problem.