ReVEL: Multi-Turn Reflective LLM-Guided Heuristic Evolution via Structured Performance Feedback
arXiv:2604.04940v2 Announce Type: replace Abstract: Designing effective heuristics for NP-hard combinatorial optimization problems remains challenging and often requires substantial domain expertise. Recent LLM-guided evolutionary methods have shown promise for automated heuristic generation, but most existing approaches refine heuristics independently or through limited pairwise feedback. We propose ReVEL: Multi-Turn Reflective LLM-Guided Heuristic Evolution via Structured Performance Feedback, a framework for group-wise multi-turn heuristic refinement. ReVEL organizes heuristics into behavio
The rapid advancements in large language models provide new capabilities for automated reasoning and feedback loops, making LLM-guided evolutionary methods for complex problem-solving increasingly viable.
This development indicates a significant step towards more sophisticated and automated AI systems capable of self-improving heuristic design, which could tackle previously intractable optimization problems.
The ability of AI to not just generate but also reflect upon and iteratively refine its own solutions in a 'group-wise multi-turn' fashion represents a leap in AI's capacity for autonomous problem-solving.
- · AI development platforms
- · Combinatorial optimization researchers
- · Industries with complex scheduling/logistics
- · LLM developers
- · Manual heuristic design consultants
- · Traditional optimization software providers (unadapted)
More efficient and effective heuristics are generated for complex computational problems.
This leads to improved performance in various applications like supply chain management, drug discovery, and materials science.
The demonstrated multi-turn reflective capability could serve as a foundational component for advanced AI agents with enhanced autonomous reasoning and adaptivity.
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Read at arXiv cs.AI