MeLA: A Metacognitive LLM-Driven Architecture for Automatic Heuristic Design

Zishang Qiu; Xinan Chen; Long Chen; Ruibin Bai

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arXiv · 2507.20541

MeLA: A Metacognitive LLM-Driven Architecture for Automatic Heuristic Design

Zishang Qiu·Xinan Chen·Long Chen·Ruibin Bai

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Abstract

This paper introduces MeLA, a Metacognitive LLM-Driven Architecture that presents a new paradigm for Automatic Heuristic Design (AHD). Traditional evolutionary methods operate directly on heuristic code; in contrast, MeLA evolves the instructional prompts used to guide a Large Language Model (LLM) in generating these heuristics. This process of "prompt evolution" is driven by a novel metacognitive framework where the system analyzes performance feedback to systematically refine its generative strategy. MeLA's architecture integrates a problem analyzer to construct an initial strategic prompt, an error diagnosis system to repair faulty code, and a metacognitive search engine that iteratively optimizes the prompt based on heuristic effectiveness. In comprehensive experiments across both benchmark and real-world problems, MeLA consistently generates more effective and robust heuristics, significantly outperforming state-of-the-art methods. Ultimately, this research demonstrates the profound potential of using cognitive science as a blueprint for AI architecture, revealing that by enabling an LLM to metacognitively regulate its problem-solving process, we unlock a more robust and interpretable path to AHD.

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Zishang Qiu, Xinan Chen, Long Chen, Ruibin Bai. 2025-07-28. MeLA: A Metacognitive LLM-Driven Architecture for Automatic Heuristic Design. https://arxiv.org/abs/2507.20541

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