AgenticRS-EnsNAS: Ensemble-Decoupled Self-Evolving Architecture Search
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 ...
| Authors | Yun Chen, Moyu Zhang, Jinxin Hu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
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