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

arXiv 2602.18985 ↗First posted 22 Feb 2026cs.AIcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 22 Feb 2026.

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References · 17

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  3. 2023GAIA: a benchmark for General AI Assistantscited by 1,084
  4. 2023Teaching Large Language Models to Self-Debugcited by 963
  5. 2023ChemCrow: Augmenting large-language models with chemistry toolscited by 609
  6. 2024Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Modelcited by 258
  7. 2024Tool Learning with Large Language Models: A Surveycited by 221
  8. 2024MapCoder: Multi-Agent Code Generation for Competitive Problem Solvingcited by 177
  9. 2023GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Informationcited by 115
  10. 2024SciAgent: Tool-augmented Language Models for Scientific Reasoningcited by 69
  11. 2025OctoTools: An Agentic Framework with Extensible Tools for Complex Reasoningcited by 69
  12. 2025SciToolAgent: A Knowledge Graph-Driven Scientific Agent for Multi-Tool Integrationcited by 40
5 more
  1. 2023CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generationcited by 29
  2. 2025Automated Algorithmic Discovery for Scientific Computing through LLM-Guided Evolutionary Search: A Case Study in Gravitational-Wave Detectioncited by 9
  3. 2025PDE-Agent: A toolchain-augmented multi-agent framework for PDE solvingcited by 7
  4. 2025EvoVLMA: Evolutionary Vision-Language Model Adaptationcited by 1
  5. 2025NMIRacle: Multi-modal Generative Molecular Elucidation from IR and NMR Spectracited by 1

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