Academic

ReVEL: Multi-Turn Reflective LLM-Guided Heuristic Evolution via Structured Performance Feedback

arXiv:2604.04940v1 Announce Type: new Abstract: Designing effective heuristics for NP-hard combinatorial optimization problems remains a challenging and expertise-intensive task. Existing applications of large language models (LLMs) primarily rely on one-shot code synthesis, yielding brittle heuristics that underutilize the models' capacity for iterative reasoning. We propose ReVEL: Multi-Turn Reflective LLM-Guided Heuristic Evolution via Structured Performance Feedback, a hybrid framework that embeds LLMs as interactive, multi-turn reasoners within an evolutionary algorithm (EA). The core of ReVEL lies in two mechanisms: (i) performance-profile grouping, which clusters candidate heuristics into behaviorally coherent groups to provide compact and informative feedback to the LLM; and (ii) multi-turn, feedback-driven reflection, through which the LLM analyzes group-level behaviors and generates targeted heuristic refinements. These refinements are selectively integrated and validated by

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Cuong Van Duc, Minh Nguyen Dinh Tuan, Tam Vu Duc, Tung Vu Duy, Son Nguyen Van, Hanh Nguyen Thi, Binh Huynh Thi Thanh · · 1 min read · 3 views
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arXiv:2604.04940v1 Announce Type: new Abstract: Designing effective heuristics for NP-hard combinatorial optimization problems remains a challenging and expertise-intensive task. Existing applications of large language models (LLMs) primarily rely on one-shot code synthesis, yielding brittle heuristics that underutilize the models' capacity for iterative reasoning. We propose ReVEL: Multi-Turn Reflective LLM-Guided Heuristic Evolution via Structured Performance Feedback, a hybrid framework that embeds LLMs as interactive, multi-turn reasoners within an evolutionary algorithm (EA). The core of ReVEL lies in two mechanisms: (i) performance-profile grouping, which clusters candidate heuristics into behaviorally coherent groups to provide compact and informative feedback to the LLM; and (ii) multi-turn, feedback-driven reflection, through which the LLM analyzes group-level behaviors and generates targeted heuristic refinements. These refinements are selectively integrated and validated by an EA-based meta-controller that adaptively balances exploration and exploitation. Experiments on standard combinatorial optimization benchmarks show that ReVEL consistently produces heuristics that are more robust and diverse, achieving statistically significant improvements over strong baselines. Our results highlight multi-turn reasoning with structured grouping as a principled paradigm for automated heuristic design.

Executive Summary

The article introduces ReVEL, a novel framework that leverages large language models (LLMs) as multi-turn reflective reasoners within an evolutionary algorithm (EA) to design effective heuristics for NP-hard combinatorial optimization problems. By employing performance-profile grouping and structured performance feedback, ReVEL clusters candidate heuristics into behaviorally coherent groups, enabling the LLM to generate targeted refinements iteratively. The EA meta-controller then selectively integrates these refinements, balancing exploration and exploitation. Empirical results on standard benchmarks demonstrate that ReVEL produces more robust and diverse heuristics, outperforming strong baselines with statistically significant improvements. The work underscores the potential of multi-turn reasoning with structured feedback as a paradigm for automated heuristic design.

Key Points

  • ▸ ReVEL integrates LLMs as iterative, multi-turn reasoners within an evolutionary algorithm to refine heuristics for NP-hard combinatorial optimization problems, addressing the limitations of one-shot code synthesis approaches.
  • ▸ The framework employs two core mechanisms: performance-profile grouping, which clusters heuristics based on behavioral coherence to provide compact and informative feedback, and multi-turn, feedback-driven reflection, where the LLM analyzes group-level behaviors to generate targeted refinements.
  • ▸ Experiments on standard benchmarks reveal that ReVEL produces heuristics that are both more robust and diverse, achieving statistically significant improvements over strong baselines, highlighting the efficacy of structured, iterative reasoning in heuristic design.

Merits

Novelty in Hybrid AI Integration

ReVEL uniquely combines LLMs with evolutionary algorithms in a multi-turn reflective framework, leveraging the strengths of both paradigms—LLMs' iterative reasoning and EAs' adaptive search—to address the brittleness of one-shot heuristic generation.

Structured Performance Feedback

The introduction of performance-profile grouping to cluster heuristics and provide compact, group-level feedback is a principled innovation that enhances the LLM's ability to generate meaningful and targeted refinements.

Empirical Robustness and Diversity

The framework demonstrates consistent improvements in heuristic robustness and diversity across standard benchmarks, validated through statistically significant results, underscoring its practical utility and scalability.

Demerits

Computational Overhead

The multi-turn, iterative nature of ReVEL, while enhancing reasoning depth, introduces significant computational overhead compared to one-shot heuristic generation methods, potentially limiting scalability for very large problem instances.

Dependence on LLM Capabilities

The effectiveness of ReVEL is contingent on the reasoning and generalization capabilities of the underlying LLM. Suboptimal LLM performance in certain domains may constrain the framework's ability to generate high-quality refinements.

Benchmark-Specific Performance

While results on standard benchmarks are promising, the generalizability of ReVEL's performance to real-world, domain-specific NP-hard problems remains to be thoroughly validated.

Expert Commentary

ReVEL represents a significant leap in the integration of LLMs with evolutionary algorithms, addressing a critical gap in the automated design of heuristics for NP-hard problems. The introduction of performance-profile grouping and multi-turn reflection is particularly noteworthy, as it transforms the LLM from a passive generator of code into an active, iterative reasoner that can adaptively refine its outputs based on structured feedback. This approach not only enhances the quality of the generated heuristics but also provides a more interpretable and explainable process, which is often lacking in black-box optimization methods. However, the computational demands of ReVEL and its reliance on the underlying LLM’s capabilities may pose challenges for widespread adoption. Future work should explore methods to reduce computational overhead, such as model distillation or efficient sampling strategies, and investigate the framework’s applicability to domain-specific problems. Overall, ReVEL sets a new benchmark for hybrid AI systems in optimization, paving the way for more robust and adaptive automated heuristic design.

Recommendations

  • ✓ Conduct further empirical validation of ReVEL across a broader range of NP-hard problems, including real-world industrial applications, to assess its generalizability and scalability.
  • ✓ Explore hybrid architectures that combine ReVEL’s reflective feedback mechanisms with smaller, domain-specific LLMs or fine-tuned models to mitigate computational overhead while preserving performance.
  • ✓ Develop standardized benchmarks and evaluation metrics specifically tailored to multi-turn, feedback-driven heuristic design to facilitate fair comparisons with existing methods.
  • ✓ Investigate the integration of human-in-the-loop validation within ReVEL to enhance interpretability and trust, particularly in high-stakes applications such as healthcare or finance.
  • ✓ Study the potential for transfer learning within ReVEL, where heuristics or feedback mechanisms learned in one domain can be adapted to others, reducing the need for extensive retraining.

Sources

Original: arXiv - cs.AI

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