自验证自优化引擎让红外计算自动完成，效率提升21倍。
InfEngine: A Self-Verifying and Self-Optimizing Intelligent Engine for Infrared Radiation Computing
- 用联合求解器与评估器实现代码自我验证，确保结果科学合理。
- 通过自发现目标函数的进化算法，自动优化计算流程性能。
- 适合气候、遥感等领域的科研人员，降低计算门槛。
红外辐射计算支撑气候科学、遥感和光谱学的发展，但长期受限于人工流程。本文提出 InfEngine，一种自主智能计算引擎，推动从人工主导转向人机协同自动化。它通过两大创新集成四个专用智能体：自验证机制（基于求解器-评估器联合调试）显著提升功能正确性与科学合理性；自优化机制（采用进化算法并自发现适应度函数）实现性能自主优化。在包含200个红外特化任务的InfBench上测试，结合270个精选工具的InfTools，InfEngine达到92.7%的任务通过率，计算流程速度比人工专家快21倍。更重要的是，它展示了研究者如何从手动编码转向与可验证、可优化的计算伙伴协作。生成的代码可复用、已验证且高效，使计算工作流成为可持续的科学资产，加速科学发现进程。代码开源：https://github.com/kding1225/infengine
原文摘要 · Abstract (English)
Infrared radiation computing underpins advances in climate science, remote sensing and spectroscopy but remains constrained by manual workflows. We introduce InfEngine, an autonomous intelligent computational engine designed to drive a paradigm shift from human-led orchestration to collaborative automation. It integrates four specialized agents through two core innovations: self-verification, enabled by joint solver-evaluator debugging, improves functional correctness and scientific plausibility; self-optimization, realized via evolutionary algorithms with self-discovered fitness functions, facilitates autonomous performance optimization. Evaluated on InfBench with 200 infrared-specific tasks and powered by InfTools with 270 curated tools, InfEngine achieves a 92.7% pass rate and delivers workflows 21x faster than manual expert effort. More fundamentally, it illustrates how researchers can transition from manual coding to collaborating with self-verifying, self-optimizing computational partners. By generating reusable, verified and optimized code, InfEngine transforms computational workflows into persistent scientific assets, accelerating the cycle of scientific discovery. Code: https://github.com/kding1225/infengine
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