The paper introduces a semi-automated method for structuring neural architecture search (NAS) using large language models (LLMs). It defines a high-level template of architectural attributes, which an LLM populates by analyzing research papers to create a diverse search space. The authors then propose FairNAD, a multi-type mutation strategy that combines fair idea sampling, Pareto-aware mutation, LLM-driven iterative mutation, and a fine-grained feedback loop to efficiently explore this space, achieving significant performance gains on CIFAR-10, CIFAR-100, and ImageNet16-120.
arXiv:2608. 01633v1 Announce Type: new Abstract: Large language models (LLMs) enable neural architecture search (NAS) directly over executable neural network programs.
ONNX-Net introduces a universal representation for neural architectures using natural language descriptions, enabling instant performance prediction across diverse search spaces. The authors present ONNX-Bench, a benchmark of over 600k architecture–accuracy pairs compiled from open‑source NAS‑bench networks in ONNX format. Experiments demonstrate strong zero‑shot predictive performance with minimal pretraining, overcoming the limitations of cell‑based, graph‑encoded approaches.
arXiv:2507. 15336v3 Announce Type: replace-cross Abstract: Designing high-performance neural networks for new tasks requires balancing optimization quality with search efficiency.
arXiv:2607. 06839v1 Announce Type: new Abstract: Existing NAS benchmarks (e.
arXiv:2605. 04057v3 Announce Type: replace-cross Abstract: This paper focuses on a key challenge in Neural Architecture Search (NAS): integrating established architectural knowledge while exploring new designs under expensive evaluations.
SyntheticHLS is a framework that uses large language models to create large-scale, diverse synthetic high‑level synthesis (HLS) datasets. It employs an iterative, feedback‑guided mutation loop that transforms seed designs into more complex, scalable ones, guided by quantitative metrics of design complexity and scalability. The resulting datasets outperform manually curated or zero‑shot generated ones in training deep learning models for HLS quality‑of‑results, offering broader coverage of design space and better generalization.
arXiv:2606. 10294v1 Announce Type: cross Abstract: Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as energy cost, physical non-idealities, and numerical precision.
MIDAS is a differentiable neural architecture search method that replaces static architecture parameters with dynamic, input‑specific parameters generated through self‑attention. It localizes architecture selection to each spatial patch of the activation map and introduces a parameter‑free, topology‑aware search space that models two‑edge node topology. Evaluations on DARTS, NAS‑Bench‑201, and RDARTS show that MIDAS achieves state‑of‑the‑art accuracy on CIFAR‑10 and CIFAR‑100 and discovers optimal or near‑optimal architectures in benchmark spaces.
arXiv:2607. 07984v1 Announce Type: new Abstract: Neural architecture search (NAS) methods have grown increasingly efficient, yet they remain bounded by manually engineered search spaces that require substantial domain expertise and must be rebuilt for every new task.
EvoTreeNAD is a genealogy‑guided evolutionary algorithm that autonomously discovers neural architectures without a predefined seed or search space. Starting from an empty root, it builds a persistent genealogy where each node represents a complete architecture; top‑percentile values from nodes and descendants steer lineage selection. The method combines an Idea Agent that proposes variants and a Code Agent that implements them, with theoretical analysis showing stationary variation regimes and empirical results demonstrating superior performance on CIFAR‑10/100 and MedMNIST‑v2 tasks.
arXiv:2606. 29520v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used as assistants across the software development lifecycle, yet their ability to reason about software architecture remains largely unmeasured.
arXiv:2511. 22651v2 Announce Type: replace-cross Abstract: Optimization methods have long advanced many fields, yet they struggle when faced with design problems where the search space and design parameters are difficult to define.