arXiv cs.AI· Pengcheng Hou, Xiaojun Tan, Sihan Hu, Ruisi Wang, Shuo Chen, Lei Wang, Youjin Deng, Kun Chen·· 3 天前AI 评分31
面向可微科学软件生态的 AI 智能体递归改进环境
Recursive Improvement of a Differentiable Scientific Software Ecosystem
AI 导读
研究者提出一个由 AI 智能体驱动的可微科学软件演化环境，将需求识别、开发与质量评估打通，通过统一微分接口复用导数规则。该工作为物理、化学、生物建模领域的 20 个软件包构建并验证了自动微分扩展，基准测试显示梯度评估与参数估计相比有限差分更省算力。研究驱动的修订使原本不支持的流程实现可微，并修正了科学观测量的导数、消除冗余计算。
正文 · 原文
Abstract:Differentiable programming connects scientific computation with gradient-based inference, learning and design. Extending these capabilities across a heterogeneous software ecosystem requires specialized effort to implement derivatives, integrate interfaces and evaluate quality. AI coding agents can accelerate this transformation, but translating their capabilities into useful scientific software requires identifying research needs and evaluating how well implementations meet them. We present an environment for agent-driven evolution of differentiable scientific software that connects demand identification, development and quality evaluation. A unified differentiation interface exposes reusable derivative rules alongside existing numerical routines, allowing research tasks to share these capabilities. Research requirements guide development, with implementations assessed through independent derivative checks, workflow tests and performance evaluation. Validated software, research programs and tests become shared resources for subsequent studies. We construct and validate automatic differentiation extensions across 20 packages spanning physical, chemical and biological modeling, with research workflows demonstrating reuse across tasks. Benchmarks demonstrate computational savings over finite differences in gradient evaluation and complete parameter estimation. Research-driven revisions make previously unsupported workflows differentiable, correct derivatives of scientific observables and eliminate redundant computation. Quantum-control and thermal-design studies revise objectives in response to physical evaluation, improving designs while reusing existing derivatives. This work provides a practical approach to expanding differentiable programming across established scientific software and organizing AI agents around the recursive improvement of a shared computational ecosystem.
| Subjects: | Artificial Intelligence (cs.AI); Computational Physics (physics.comp-ph) |
| Cite as: | arXiv:2610.04561 [cs.AI] |
| (or arXiv:2610.04561v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.04561 arXiv-issued DOI via DataCite |
Submission history
From: Pengcheng Hou [view email]
[v1]
Sat, 3 Oct 2026 14:51:09 UTC (728 KB)
[v2]
Mon, 12 Oct 2026 08:01:04 UTC (728 KB)
来源：arXiv cs.AI · arxiv.org