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

TL;DR

InfEngine is an autonomous computational engine that uses self-verification and self-optimization to automate infrared radiation computing workflows, significantly improving efficiency and correctness in scientific tasks.

Contribution

It introduces a novel framework combining self-verification and self-optimization for autonomous infrared computing workflows, enabling collaborative automation and scientific asset creation.

Findings

Achieves 92.7% pass rate on InfBench tasks

Delivers workflows 21 times faster than manual effort

Demonstrates effective autonomous performance optimization

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