arXiv cs.CL· Chaipat Tirapongprasert, Matthew Ho·· 11 小时前AI 评分26
AgenticTuner：用 LLM 智能体校准宇宙学模拟
Agentic Systems for Calibrating Cosmological Simulations
AI 导读
研究者提出 AgenticTuner，一个由现成 LLM 担任优化器的智能体校准流程，负责提出下一组参数试验，用于宇宙学模拟的高维参数优化。在给定代码库、文档和启发式指南等非正式人类可读先验时，该智能体以仅一小部分 CPU 成本达到有竞争力的校准表现。该工作已被 ML4PS 研讨会接收，并提供起步代码。
正文 · 原文
Abstract:Calibrating a cosmological simulation is an expensive, high-dimensional parameter optimization task. Previous works employ Bayesian optimization for automation, which treats the simulator as a black box and precludes the incorporation of human domain knowledge or workflow into the selection policy. We propose AgenticTuner, an agentic calibration pipeline with a present-day large language model serving as the optimizer who proposes the next parameter trial. We show that these agents can achieve competitive calibration performance at a fraction of the CPU cost when they are given informal, human-readable priors (such as codebase, documentation, and heuristic guides that seasoned scientists may possess). We hope this work serves as an early step toward AI scientists that can carry out complex, long-horizon scientific tasks.
| Comments: | Accepted to the 9th annual workshop for Machine Learning & the Physical Sciences (ML4PS). A starter code is available on this https URL |
| Subjects: | Instrumentation and Methods for Astrophysics (astro-ph.IM); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.40685 [astro-ph.IM] |
| (or arXiv:2610.40685v1 [astro-ph.IM] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40685 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Chaipat Tirapongprasert [view email]
[v1]
Mon, 26 Oct 2026 21:39:46 UTC (283 KB)
来源：arXiv cs.CL · arxiv.org