[2605.04057] Structured Progressive Knowledge Activation for LLM-Driven Neural Architecture Search
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Computer Science > Machine Learning
arXiv:2605.04057 (cs)
[Submitted on 10 Apr 2026]
Title:Structured Progressive Knowledge Activation for LLM-Driven Neural Architecture Search
Authors:Zhen Liu, Yuhan Liu, Jingwen Fu
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Abstract:This paper focuses on a key challenge in Neural Architecture Search (NAS): integrating established architectural knowledge while exploring new designs under expensive evaluations. Large language models (LLMs) are a promising assistant for NAS because they can translate rich architectural and coding priors into executable code edits. However, in practice, seemingly local revisions often propagate into non-local behavioral and performance shifts because a single edit can inadvertently couple multiple interacting functional factors, a phenomenon we refer to as functional entanglement. To make LLM knowledge usable under such entanglement, we propose Structured Progressive Knowledge Activation (SPARK), which activates relevant priors by explicitly selecting the functional factor to modify and conditioning the edit on that factor. This factor-conditioned editing reduces entangled side effects and yields more targeted, reliable architecture modifications. On CLRS-DFS, SPARK achieves a 28.1x sample-efficient architecture evolution speedup and yields a 22.9 percent relative improvement in OOD accuracy.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.04057 [cs.LG]
(or arXiv:2605.04057v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.04057
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arXiv-issued DOI via DataCite
Submission history
From: Zhen Liu [view email] [v1] Fri, 10 Apr 2026 07:40:23 UTC (590 KB)
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