PathWise: Planning through World Model for Automated Heuristic Design via Self-Evolving LLMs
arXiv:2601.20539v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have enabled automated heuristic design (AHD) for combinatorial optimization problems (COPs), but existing frameworks' reliance on fixed evolutionary rules and static prompt templates often leads to myopic heuristic generation, redundant evaluations, and limited reasoning about how new heuristics should be derived. We propose a novel multi-agent reasoning framework, referred to as Planning through World Model for Automated Heuristic Design via Self-Evolving LLMs (PathWise), which formulates heuristic generat
The rapid advancement of LLMs has reached a point where their capabilities can be leveraged for more sophisticated, self-evolving automated heuristic design methods.
This research signifies a step towards more capable and autonomous AI systems that can optimize complex problems with less human intervention, potentially accelerating scientific discovery and operational efficiency.
Existing frameworks for automated heuristic design, previously limited by static rules, are being supplanted by dynamic, self-evolving LLM-based approaches, leading to more robust and adaptable problem-solving.
- · AI researchers
- · Optimization software developers
- · Industries reliant on combinatorial optimization
- · Traditional heuristic optimization methods
- · Fixed-rule AI systems
More efficient solutions for complex combinatorial problems across various sectors.
Reduced need for human experts in designing optimization algorithms, redirecting their efforts to higher-level challenges.
The development of highly autonomous AI agents capable of continuous self-improvement in problem-solving domains.
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Read at arXiv cs.CL