Scaling Closed-Loop Feature Channel Configuration with LLMs
A new study scales LLM-based closed-loop channel configuration search to 250 candidates per cycle, confirming transferable optimization and revealing architectural regularities such as non-power-of-two widths and structured allocation patterns.
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[Submitted on 7 Jul 2026]
Title:Scaling Closed-Loop Feature Channel Configuration with LLMs
View a PDF of the paper titled Scaling Closed-Loop Feature Channel Configuration with LLMs, by Tolgay Atinc Uzun and 2 other authors
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Abstract:Promising initial results in closed-loop large-language-model-based channel-configuration search demonstrated that neural-network widths can be optimized directly through executable code generation and accuracy feedback. However, those results were obtained from a relatively sparse set of valid evaluations, leaving open whether the observed optimization behavior transfers to a denser sampling regime and whether additional architectural regularities emerge when more generated networks are evaluated. To test this, the same search setting is scaled to 250 candidate networks per fine-tuning cycle. The analysis covers 2000 generated candidates from 8 complete cycles, yielding 462 verified CIFAR-100 evaluations after task and metadata filtering. Per-cycle mean accuracy exhibits a positive linear trend with slope 9.87e-4 (p=0.043), while the high-performing frontier improves more strongly: the best observed accuracy increases from 0.3144 to 0.3676, and both the top-5 and top-10 cycle-level means exhibit positive trends. The scaled run also reveals improved parameter efficiency. The best model reaches 0.3676 with 11.8M parameters, compared with an early high-performing model at 0.3144 with 166.5M parameters. Beyond accuracy, the larger sample exposes architectural regularities that were difficult to assess from sparse observations. Non-power-of-two channel widths occur in 41.8% of verified candidates, and the strongest models share structured channel-allocation patterns characterized by moderate early widths and expanded middle or later blocks. These findings indicate that the channel-search signal observed in the initial study transfers
Comments: 15 pages, 8 figures
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.20516 [cs.LG]
(or arXiv:2607.20516v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.20516
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tolgay Atinc Uzun [view email] [v1] Tue, 7 Jul 2026 22:31:53 UTC (2,073 KB)
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