---
id: 20260302-T0-21
title: "Meta-Evolution工具EvoX发布：自动化优化算法准确率提升35%"
title_en: "EvoX Tool Boosts Algorithm Optimization Accuracy by 35%"
url: https://ai.daily.yangsir.net/daily/20260302-T0-21
issue_date: 2026-03-02
publish_date: 2026-03-02T05:00:00.000Z
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2602.23413
---

# Meta-Evolution工具EvoX发布：自动化优化算法准确率提升35%

Meta研究人员推出EvoX工具，结合LLM优化与进化搜索，实现跨领域算法自动化改进。实验显示，该工具在程序生成、提示优化和算法设计任务中，平均性能提升35%，优于现有AlphaEvolve方案。EvoX通过复用历史评估数据加速优化，适用于AI模型调优、自动化代码生成等场景，开发者可直接使用其API集成到现有工作流。

## English Version

**EvoX Tool Boosts Algorithm Optimization Accuracy by 35%**

Meta researchers released EvoX, a tool combining LLM optimization with evolutionary search for cross-domain algorithm automation. Experiments show 35% average performance improvements in program generation, prompt optimization, and algorithm design tasks, outperforming existing AlphaEvolve solutions. EvoX speeds up optimization by reusing historical evaluation data and is suitable for AI model tuning and automated code generation. Developers can use its API to integrate into existing workflows.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2602.23413)

**详情页**：https://ai.daily.yangsir.net/daily/20260302-T0-21

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