InfEngine: A Self-Verifying and Self-Optimizing Intelligent Engine for Infrared Radiation Computing

2026-02-21

AI Summary

This work addresses the longstanding reliance on inefficient and error-prone manual workflows in infrared radiative transfer calculations, which hinders the demand for fast and reliable computation in climate science and remote sensing. The authors propose InfEngine, an autonomous intelligent computing engine that automates task execution, self-validation, and self-optimization through multi-agent collaboration. Scientific correctness is ensured via a solver-evaluator co-debugging mechanism, while workflow optimization is achieved through an evolutionary algorithm guided by a self-discovered fitness function. Integrated with four specialized agents, 270 tools, and a dedicated benchmark suite (InfBench), InfEngine achieves a 92.7% pass rate across 200 tasks and generates code 21 times faster than expert manual implementation. This system pioneers the transformation of reusable, verified code into persistent scientific assets, advancing research workflows toward a human–AI collaborative paradigm.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Multiagent Systems: Teamwork

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

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 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

Problem

• infrared radiation computing
• manual workflows
• computational automation
• scientific correctness
• workflow optimization

Innovation

• self-verification
• self-optimization
• infrared radiation computing
• autonomous intelligent engine
• evolutionary algorithms