![[2602.18985] InfEngine: A Self-Verifying and Self-Optimizing Intelligent Engine for Infrared Radiation Computing](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.18985] InfEngine: A Self-Verifying and Self-Optimizing Intelligent Engine for Infrared Radiation Computing
Summary
InfEngine is an innovative autonomous engine designed to enhance infrared radiation computing by automating workflows, achieving a 92.7% pass rate and significantly speeding up processes compared to manual efforts.
Why It Matters
This research highlights a significant advancement in computational efficiency for infrared radiation tasks, which are crucial in fields like climate science and remote sensing. By transitioning from manual to automated systems, it paves the way for faster scientific discoveries and improved accuracy in data processing.
Key Takeaways
- InfEngine integrates self-verification and self-optimization for enhanced performance.
- Achieves a 92.7% pass rate on 200 infrared-specific tasks.
- Delivers workflows 21 times faster than traditional manual methods.
- Transforms computational workflows into reusable scientific assets.
- Encourages collaboration between researchers and autonomous systems.
Computer Science > Artificial Intelligence arXiv:2602.18985 (cs) [Submitted on 22 Feb 2026] Title:InfEngine: A Self-Verifying and Self-Optimizing Intelligent Engine for Infrared Radiation Computing Authors:Kun Ding, Jian Xu, Ying Wang, Peipei Yang, Shiming Xiang View a PDF of the paper titled InfEngine: A Self-Verifying and Self-Optimizing Intelligent Engine for Infrared Radiation Computing, by Kun Ding and Jian Xu and Ying Wang and Peipei Yang and Shiming Xiang View PDF HTML (experimental) Abstract: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 compu...