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

• URL: http://arxiv.org/abs/2507.20541v2
• Date: Sun, 03 Aug 2025 07:31:26 GMT
• Title: MeLA: A Metacognitive LLM-Driven Architecture for Automatic Heuristic Design
• Authors: Zishang Qiu, Xinan Chen, Long Chen, Ruibin Bai,
• Abstract summary: MeLA is a Metacognitive LLM-Driven Architecture that presents a new paradigm for Automatic Heuristic Design (AHD)<n>MeLA evolves the instructional prompts used to guide a Large Language Model (LLM) in generating theses.<n>This process of "prompt evolution" is driven by a novel metacognitive framework.
• Score: 8.025492778235199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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