LegoSR：统一框架实现智能体引导的科学发现自我改进
LegoSR: A Unified Framework for Self-improving Agent-Guided Scientific Discovery
AI 导读
研究者提出 LegoSR，一个统一的代数符号回归与微分方程发现框架，通过统一的方程表示和评估流程整合七个求解器，并支持基于部分已知方程的约束搜索。LegoSR Agent 能根据求解器知识、中间结果和先前任务经验自动选择求解器并调整搜索策略。
正文 · 原文
Abstract:Equation discovery solvers often use different data interfaces, equation representations and evaluation criteria, making their results difficult to compare. We present LegoSR, a unified framework for algebraic symbolic regression and differential equation discovery. LegoSR integrates seven solvers through a common equation representation and evaluation process, and supports searches constrained by partially known equations. Researchers can run solvers directly or use LegoSR Agent, which selects solvers and adapts searches based on solver knowledge, intermediate results and lessons from earlier tasks. On noisy ordinary and partial differential equation benchmarks, LegoSR Agent recovers nearly as many equations as the union of seven expert-tuned solvers and outperforms a generic agent with the same model, tools and budget by up to 29 percentage points. Three examples demonstrate basic use, adaptive solver selection and discovery guided by partial physical knowledge.
| Comments: | 36 pages, 6 figures. Code is available at this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.33465 [cs.AI] |
| (or arXiv:2610.33465v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33465 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hao Mao [view email]
[v1]
Sat, 24 Oct 2026 05:36:55 UTC (437 KB)
来源：arXiv cs.AI · arxiv.org