# Math-Inference: Enabling Formal Verification of Large Language Models' Math Answers

> An open-source project based on Phoenix LiveView that combines LLM routing capabilities with mathematical proof engines like SymPy, Julia, Octave, and Lean4 to achieve formal verification of AI-generated math answers, providing reliability guarantees for math AI applications.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-15T00:11:55.000Z
- 最近活动: 2026-04-15T00:29:21.585Z
- 热度: 154.7
- 关键词: LLM, 数学验证, 形式化证明, Lean 4, SymPy, Phoenix LiveView, Julia, 自动定理证明, AI安全, 数学推理
- 页面链接: https://www.zingnex.cn/en/forum/thread/math-inference
- Canonical: https://www.zingnex.cn/forum/thread/math-inference
- Markdown 来源: floors_fallback

---

## Introduction: Math-Inference—Enabling Formal Verification of LLM Math Answers

Math-Inference is an open-source project based on Phoenix LiveView. By combining LLM routing capabilities with mathematical proof engines such as SymPy, Julia, Octave, and Lean4, it implements formal verification of AI-generated math answers through a two-layer generation-verification architecture, providing reliability guarantees for math AI applications and solving the hallucination problem in LLM math outputs.

## Background: Dilemmas and Needs of LLM Math Reasoning

LLMs have a "hallucination" problem in math reasoning, generating content that seems correct but is actually wrong. Traditional solutions (adding data, modifying architecture, fine-tuning) cannot guarantee absolute correctness, and math has zero tolerance for errors—thus the industry needs technical solutions to verify LLM math outputs.

## Project Overview and Core Technical Architecture

Math-Inference uses the Phoenix LiveView framework (developed in Elixir) to build real-time interactive applications. Its core architecture consists of three layers:
1. **LLM Routing Layer**: Intelligently distributes problems—returns results directly for simple questions and triggers verification for complex ones;
2. **Multi-Engine Verification Layer**: Complementary use of SymPy (symbolic computation), Julia (numerical computation), Octave (engineering computation), and Lean4 (formal proof);
3. **Coordination Mechanism**: Hierarchical verification strategy to optimize resource utilization.

## Technical Implementation Highlights: Real-Time Interaction and Intelligent Loop

Highlights include:
1. **Real-Time Interaction**: Phoenix LiveView pushes the verification process via WebSocket to enhance user trust;
2. **Feedback Loop**: Verification errors are fed back to the LLM for re-generation, improving answer quality;
3. **Scalable Architecture**: Modular design makes it easy to integrate new engines (e.g., Coq, Isabelle).

## Application Scenarios and Value: Multi-Domain Support

Application scenarios:
1. **Educational Assistance**: Intelligent teaching assistants—verified answers are used for automatic grading;
2. **Research Verification**: Quickly check intermediate steps of scientific computing;
3. **Theorem Proving**: Provide proof suggestions and verification services in combination with Lean4.

## Limitations and Future Outlook

Limitations: High verification cost (complex proofs take time), and LLM-generated Lean code has many syntax errors. Future directions: Optimize verification strategies, improve LLM formal code generation, and explore lightweight verification methods.

## Conclusion: AI Direction Emphasizing Both Generation and Verification

Math-Inference represents the shift of AI math applications from generation-only to emphasizing both generation and verification, providing a reference for high-correctness AI scenarios. We look forward to more reliable intelligent systems emerging.