Latest Neural Architecture Search Research Papers
The newest Neural Architecture Search papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Neural Architecture Search so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.
Get the latest Neural Architecture Search papers in your inbox — free →Recent papers
- HEC-NAS-FDS: hybrid expert-conditioned exhaustive neural network architecture search over finite design spaceEva Holasová, Radek Fujdiak, Petr Mlýnek · Scientific Reports · Jul 17, 2026
This article presents a new proof-of-concept method called Hybrid Expert-Conditioned Exhaustive Neural Network Architecture Search over Finite Design Space (HEC-NAS-FDS), which aims to find a suitable deep neural network (DNN) architecture …
- HiFi-LLP: High-Fidelity, Low-Cost Latency Predictors with Confidence for Robust HW-NASShambhavi Balamuthu Sampath, Behzad Shomali, Nael Fasfous, Moritz Thoma et al. · arXiv · Jul 13, 2026
With deep neural networks (DNNs) increasingly deployed on edge devices, hardware (HW)-aware optimization techniques--such as HW-aware compression and HW-aware neural architecture search (HW-NAS)--have become essential. These methods rely on…
- Hybrid encoding and multi-objective optimization-based neural architecture search for object detectionLiang Wang, Yongjie Ma, Tao Gong, Quanxiu Li · International Journal of Ma... · Jul 9, 2026
- TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific ScenariosHong Lyu, Mingru Yang, Qianhua He, Yanxiong Li et al. · arXiv · Jul 7, 2026
There are some datasets of varying scales for audio classification (AC) applied to different tasks. However, annotated data is limited for most scenarios, such as domestic environments. To address this challenge, we propose an $\textbf{A}$u…
- Hedgementation = Hedgerow Segmentation: A Remote Sensing BenchmarkNathan Senyard, Salem Hamdani, Astrid Zhang, Derek Wang et al. · arXiv · Jun 22, 2026
We propose Hedgementation: a new benchmark to evaluate machine learning models for hedgerow mapping from remote sensing data at country scale and 10m$^2$ spatial resolution. We combine and harmonize multiple remote sensing data products and…
- Scaling Linear Mode Connectivity and Merging to Billion Parameter Pretrained TransformersTianyi Li, Zhiqiang Shen · arXiv · Jun 22, 2026
Linear mode connectivity (LMC) provides a promising foundation for understanding and merging independently trained neural networks, but existing methods typically optimize the interpolation path from only one model endpoint, limiting their …
- Agentic Neural Architecture SearchSeokhoon Jeong, Mijung Kim, Taehwan Kim · ICML 2026 Workshop DL4C Poster · Jun 16, 2026
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. Large language models…
- Claw-SWE-Bench: A Benchmark for Evaluating OpenClaw-style Agent Harnesses on Coding TasksMengyu Zheng, Kai Han, Boxun Li, Haiyang Xu et al. · arXiv · Jun 10, 2026
General-purpose agents such as OpenClaw are increasingly used as autonomous tool users, but their coding ability is difficult to measure under SWE-bench: a generic agent does not by itself satisfy the clean Docker workspace, patch, and pred…
- OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinibAbhijoy Sarkar, Aarchi Singh Thakur · arXiv · Jun 9, 2026
Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computatio…
- An Evolutionary Multiobjective Neural Architecture Search Approach to Advancing Cognitive Diagnosis in Intelligent EducationShangshang Yang, Haiping Ma, Ying Bi, Ye Tian et al. · IEEE Transactions on Evolutionary Computation · Dec 1, 2025
As a pivotal technique in intelligent education systems, cognitive diagnosis (CD) serves to reveal students’ knowledge proficiency for better tackling subsequent tasks. Unfortunately, due to pursuing high model interpretability, existing ma…
- RF-DETR: Neural Architecture Search for Real-Time Detection TransformersIsaac Robinson, Peter Robicheaux, Matvei Popov, Deva Ramanan et al. · arXiv.org · Nov 12, 2025
Open-vocabulary detectors achieve impressive performance on COCO, but often fail to generalize to real-world datasets with out-of-distribution classes not typically found in their pre-training. Rather than simply fine-tuning a heavy-weight …
- Neural Architecture Search with Progressive Evaluation and Subpopulation PreservationYu Xue, Jiajie Zha, Danilo Pelusi, Peng Chen et al. · IEEE Transactions on Evolutionary Computation · Oct 1, 2025
Neural architecture search (NAS) is an effective approach for automating the design of deep neural networks. Evolutionary computation (EC) is commonly used in NAS due to its global optimization capability. However, the evaluation phase of a…
- Distilling SNN Students from ANN Teachers via Spiking Neural Architecture SearchXiaotian Song, Yanan Sun · Submitted to ICLR 2026 · Sep 19, 2025
Bridging the performance gap between Spiking Neural Networks (SNNs) and Artificial Neural Networks (ANNs) under low timesteps remains a critical challenge in the SNN community. Recent work uses either ANN-supervised training or automated ar…
- RDNAS: Robust Dual-Branch Neural Architecture SearchSongbai Liu, GuanHeng Huang · ICLR 2026 Conference Withdrawn Submission · Sep 19, 2025
Deep neural networks have achieved remarkable success but remain highly vulnerable to adversarial perturbations, posing serious challenges in safety-critical applications. We propose **RDNAS**, a robust dual-branch neural architecture searc…
- PEL-NAS: Search Space Partitioned Architecture Prompt Co-evolutionary LLM-driven Hardware-Aware Neural Architecture SearchHengyi Zhu, Grace Li Zhang, Shaoyi Huang · ICLR 2026 Conference Withdrawn Submission · Sep 17, 2025
Hardware-Aware Neural Architecture Search (HW-NAS) requires joint optimization of accuracy and latency under device constraints. Traditional supernet-based methods require multiple GPU days per dataset. Large Language Model (LLM)-driven ap…
- Jet-Nemotron: Efficient Language Model with Post Neural Architecture SearchYuxian Gu, Qinghao Hu, Shang Yang, Haocheng Xi et al. · arXiv.org · Aug 21, 2025
We present Jet-Nemotron, a new family of hybrid-architecture language models, which matches or exceeds the accuracy of leading full-attention models while significantly improving generation throughput. Jet-Nemotron is developed using Post N…
- Causal-aware Graph Neural Architecture Search under Distribution ShiftsPeiwen Li, Xin Wang, Zeyang Zhang, Yi Qin et al. · Knowledge Discovery and Data Mining · Aug 3, 2025
Graph neural architecture search (NAS) has emerged as a promising approach for autonomously designing graph neural network architectures by leveraging correlations between graphs and architectures. However, existing methods merely rely on c…
- GA-OMTL: Genetic algorithm optimization for multi-task neural architecture search in NIR spectroscopyYu Yang, Siqi Wang, Gan Zhang, Qifu Wang et al. · Expert systems with applications · Jun 1, 2025
- Neural Architecture Search-Guided Physics-Informed Neural Network for Energy Management in Hybrid Energy Storage System with Electric VehiclesM.Sivaramkrishnan, Ancelin L, M. Ramkumar, O. J. J. A. Al Jawad et al. · International Conference on Industrial Mechatronics and Automation · May 28, 2025
An efficient Energy Management (EM) of a Hybrid Energy Storage System (HESS) combining batteries and Supercapacitors (SCs) is essential for enhancing the performance and reliability of Electric Vehicles (EVs). However, challenges such as hi…
- Defying Multi-Model Forgetting in One-Shot Neural Architecture Search Using Orthogonal Gradient LearningLianbo Ma, Yuee Zhou, Ye Ma, Guo Yu et al. · IEEE transactions on computers · May 1, 2025
One-shot neural architecture search (NAS) trains an over-parameterized network (termed as supernet) that assembles all the architectures as its subnets by using weight sharing for computational budget reduction. However, there is an issue o…
- Large Language Models Enhanced Personalized Graph Neural Architecture Search in Federated LearningHui Fang, Yang Gao, Peng Zhang, Jiangchao Yao et al. · AAAI Conference on Artificial Intelligence · Apr 11, 2025
Personalized federated learning (PFL) on graphs is an emerging field focusing on the collaborative development of architectures across multiple clients, each with distinct graph data distributions while adhering to strict privacy standards.…
- Multi-objective Differentiable Neural Architecture SearchRhea Sanjay Sukthanker, Arber Zela, Benedikt Staffler, Samuel Dooley et al. · ICLR 2025 Poster · Jan 22, 2025
Pareto front profiling in multi-objective optimization (MOO), i.e., finding a diverse set of Pareto optimal solutions, is challenging, especially with expensive objectives that require training a neural network. Typically, in MOO for neural…
- Systematic review on neural architecture searchSasan Salmani Pour Avval, Nathan Eskue, Roger M. Groves, Vahid Yaghoubi · Artificial Intelligence Review · Jan 6, 2025
Machine Learning (ML) has revolutionized various fields, enabling the development of intelligent systems capable of solving complex problems. However, the process of manually designing and optimizing ML models is often time-consuming, labor…
- Neural Architecture Search Based Deepfake Detection Model using YOLOSomnath Banerjee, Bhuman Vyas, Shalini Sivasamy, Mahaboob Subhani Shaik · International Journal of Advanced Research in Science, Communication and Technology · Jan 6, 2025
Deepfakes are intentionally created to disseminate false information or serve malicious purposes. Detecting deepfakes has become increasingly difficult due to the advancing technology involved in their creation. This paper introduces a deep…
- Score Predictor-Assisted Evolutionary Neural Architecture SearchPengcheng Jiang, Yu Xue, Ferrante Neri · IEEE Transactions on Emerging Topics in Computational Intelligence · Jan 1, 2025
- A Novel Centralized Federated Deep Fuzzy Neural Network with Multi-objectives Neural Architecture Search for Epistatic DetectionXiang Wu, Yongting Zhang, K. Lai, Ming Yang et al. · IEEE transactions on fuzzy systems · Jan 1, 2025
Epistasis detection (ED) was widely used for identifying potential risk disease variants in the human genome. A statistically meaningful ED typically requires a more extensive dataset to detect complex disease-associated single nucleotide p…
- Beyond Performance: Designing a Super-Resolution Architecture Search Space and a Hybrid Multi-Objective Approach for Neural Architecture OptimizationJ. L. L. García, Raúl Monroy, V. S. Hernández, Kalyanmoy Deb · IEEE Access · Jan 1, 2025
Multi-objective neural architecture search (NAS) for super-resolution image restoration (SRIR) targets models that simultaneously deliver high-fidelity reconstructions and respect strict computational budgets—requirements that single-object…
- SceneFormer: Neural Architecture Search of Transformers for Remote Sensing Scene ClassificationLyuyang Tong, Jie Liu, Bo Du · IEEE Transactions on Geoscience and Remote Sensing · Jan 1, 2025
Deep learning-based scene classification methods have long been a key research area in remote sensing imagery due to their wide-ranging applications. Recently, Transformer models have achieved significant progress in computer vision, making…
- RZ-NAS: Enhancing LLM-guided Neural Architecture Search via Reflective Zero-Cost StrategyZipeng Ji, Guanghui Zhu, C. Yuan, Y. Huang · International Conference on Machine Learning · Jan 1, 2025
- Spiking Spatiotemporal Neural Architecture Search for EEG-Based Emotion RecognitionWei Li, Zhihao Zhu, Shitong Shao, Yao Lu et al. · IEEE Transactions on Instrumentation and Measurement · Jan 1, 2025
Spiking neural network (SNN) has the promising ability to take advantage of the spatiotemporal information from electroencephalogram (EEG) for emotion recognition. However, manually designing suitable SNN architectures needs considerable ef…