cs.LGMar 20, 2026

AgenticRS-EnsNAS: Ensemble-Decoupled Self-Evolving Architecture Search

Authors: Yun Chen, Moyu Zhang, Jinxin Hu, Yu Zhang, Xiaoyi Zeng

Organizations: Alibaba Group, Beijing, China

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Abstract

Neural Architecture Search (NAS) deployment in industrial production systems faces a fundamental validation bottleneck: verifying a single candidate architecture pi requires evaluating the deployed ensemble of M models, incurring prohibitive O(M) computational cost per candidate. This cost barrier severely limits architecture iteration frequency in real-world applications where ensembles (M=50-200) are standard for robustness. This work introduces Ensemble-Decoupled Architecture Search, a framework that leverages ensemble theory to predict system-level performance from single-learner evaluation. We establish the Ensemble-Decoupled Theory with a sufficient condition for monotonic ensemble improvement under homogeneity assumptions: a candidate architecture pi yields lower ensemble error than the current baseline if rho(pi) < rho(pi_old) - (M / (M - 1)) * (Delta E(pi) / sigma^2(pi)), where Delta E, rho, and sigma^2 are estimable from lightweight dual-learner training. This decouples architecture search from full ensemble training, reducing per-candidate search cost from O(M) to O(1) while maintaining O(M) deployment cost only for validated winners. We unify solution strategies across pipeline continuity: (1) closed-form optimization for tractable continuous pi (exemplified by feature bagging in CTR prediction), (2) constrained differentiable optimization for intractable continuous pi, and (3) LLM-driven search with iterative monotonic acceptance for discrete pi. The framework reveals two orthogonal improvement mechanisms -- base diversity gain and accuracy gain -- providing actionable design principles for industrial-scale NAS. All theoretical derivations are rigorous with detailed proofs deferred to the appendix. Comprehensive empirical validation will be included in the journal extension of this work.

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Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

AssumptionJustification
Homogeneity (same architecture)Enforced by design: all MM members are independent training runs of π\pi .
Variance stability ( σ2(π)≈σ2(πold)\sigma^{2}(\pi)\approx\sigma^{2}(\pi_{\text{old}}) )Holds for architectures within same search space; verified in Section 6 .
Independence (different seeds/data)Standard practice in ensemble training; ensures ρ(π)<1\rho(\pi)<1 .
Appendix
Table 1: Assumptions in Theorem 3.1 and Their Validity
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