RevoNAS: Reflective Evolutionary Exploration for Neural Architecture Search

Recent progress in leveraging large language models (LLMs) has enabled Neural Architecture Search (NAS) to generate new architecture not limited from manually predefined search space. Nevertheless, LLM-driven generation remains challenging: the token-level design loop is discrete and non-differentiable, preventing constructive feedback from smoothly guiding architectural improvement. These methods, in turn, commonly suffer from mode collapse into redundant structures or drift toward infeasible designs, resulting in unnecessary generation and evaluation costs. We introduce RevoNAS, a reflective evolutionary orchestrator that effectively bridges LLM-based reasoning with feedback-aligned architectural search. First, RevoNAS presents a Multi-round Multi-expert Consensus to transfer isolated design rules into meaningful architectural clues. Then, Adaptive Reflective Exploration adjusts the degree of exploration leveraging reward variance; it explores when feedback is uncertain and refines when the improvement direction becomes sufficiently clear. Finally, Pareto-guided Evolutionary Selection effectively promotes architectures that jointly optimize accuracy, efficiency, latency, confidence, and structural diversity. On NAS-Bench-201, RevoNAS achieves state-of-the-art accuracies of 95.22$\%$, 76.38$\%$, and 50.72$\%$ on CIFAR-10, CIFAR-100, and ImageNet16-120, respectively, while evaluating only five candidate architectures. Further empirical analyses support its effectiveness and practical applicability.