Neural Architecture Search

Also known as NAS

Neural Architecture Search (NAS) studies how to choose or generate a neural network’s structure so it performs well on a task without relying entirely on manual design. Common approaches include differentiable search such as DARTS, evolutionary and reinforcement-learning methods, weight-sharing supernets, and surrogate or zero-cost predictors that estimate candidate quality cheaply. Increasingly, search also accounts for latency, memory, energy, quantization, and deployment conditions, using hardware measurements or learned cost models alongside accuracy. The central tension is how to make search economical while ensuring that cheap proxies and benchmark results reliably predict performance after full training and on real devices.

Latest papers

130 papers21 papers in the last four weeks, up 75% on the four weeks before. 0.1% of all new papers.

Zero-shot Quantum Neural Architecture Search
May 12, 2026Tung Dao, Son N. Tran, Huynh Thi Thanh BinhQuantum Machine Learning Neural Architecture Search
Hanoi University of Science and Technology, Hanoi, Vietnam · Deakin University, Melbourne, Australia
XTinyU-Net: Training-Free U-Net Scaling via Initialization-Time Sensitivity
May 10, 2026Alvin Kimbowa, Moein Heidari, David Liu +1Model Compression U-Net
School of Biomedical Engineering, The University of British Columbia, Vancouver, Canada · Department of Radiology, The University of British Columbia, Vancouver, Canada · Department of Medicine, The University of British Columbia, Vancouver, Canada
From Regression to Inference: Meta-Learning Predictors for Neural Architecture Search
May 10, 2026Liping Deng, MingQing XiaoNeural Surrogate Modeling Meta-Learning
Department of Mathematics University of California, Riverside Riverside, CA 92507 USA · School of Mathematical and Statistical Sciences Southern Illinois University Carbondale Carbondale, IL 62901 USA
TAS-LoRA: Transformer Architecture Search with Mixture-of-LoRA Experts
May 8, 2026Jeimin Jeon, Hyunju Lee, Bumsub HamVision Transformer Neural Architecture Search
Yonsei University · Articron Inc. · Korea Institute of Science and Technology (KIST)
XiYOLO: Energy-Aware Object Detection via Iterative Architecture Search and Scaling
May 7, 2026Tony Tran, Richie R. Suganda, Bin HuEnergy-Efficient ML Edge Inference
Department of Research Computing University of Houston Houston, TX 77479 · Department of Electrical and Computer Engineering University of Houston Houston, TX 77479 · Department of Engineering Technology University of Houston Houston, TX 77479
Resource-Aware Evolutionary Neural Architecture Search for Cardiac MRI Segmentation
May 7, 2026Farhana Yasmin, Mahade Hasan, Haipeng Liu +3Medical Image Analysis Medical Image Segmentation
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China. · 3Eastern University, Ashulia Model Town, Dhaka, 1345, Bangladesh. · School of Software, Nanjing University of Information Science and Technology, Nanjing, China.
Delta-Based Neural Architecture Search: LLM Fine-Tuning via Code Diffs
May 6, 2026Santosh Premi Adhikari, Radu Timofte, Dmitry IgnatovLarge Language Model-Guided NAS LLM Fine-Tuning
Computer Vision Lab, CAIDAS & IFI, University of Würzburg, Germany
Learngene Search Across Multiple Datasets for Building Variable-Sized Models
May 6, 2026Boyu Shi, Junbo Zhou, Chang Liu +3Transfer Learning Neural Architecture Search
School of Computer Science and Engineering, Southeast University, Nanjing, China · Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China
HERCULES: Hardware-Efficient, Robust, Continual Learning Neural Architecture Search
May 3, 2026Matteo Gambella, Fabrizio Pittorino, Manuel RoveriHardware-Aware NAS Neural Architecture Search
Dipartimento di Elettronica, Informazione e Bioingegneria
Random Cloud: Finding Minimal Neural Architectures Without Training
Apr 29, 2026Javier Gil BlázquezNeural Architecture Search Feedforward Neural Networks
Deployment-Aligned Low-Precision Neural Architecture Search for Spaceborne Edge AI
Apr 27, 2026Parampuneet Kaur Thind, Vaibhav Katturu, Giacomo Zema +1Edge Computing Hardware-Aware NAS
1Sapienza University of Rome · 2 Φ-lab, ESA ESRIN · 4Argotec Srl +1
Neuro-evolutionary stochastic architectures in gauge-covariant neural fields
Apr 22, 2026Rodrigo Carmo TerinEvolutionary Optimization Equivariant Neural Networks
King Juan Carlos University, Facuty of Experimental Sciences and Technology, Department of Applied Physics, Av. del Alcalde de M´ostoles, 28933, Madrid, Spain
Super Apriel: One Checkpoint, Many Speeds
Apr 21, 2026SLAM Labs, :, Oleksiy Ostapenko +13LLM Inference Acceleration Attention Mechanisms
SLAM Labs · ServiceNow Research · University of Toronto +4
LLM as a Tool, Not an Agent: Code-Mined Tree Transformations for Neural Architecture Search
Apr 17, 2026Masakazu Yoshimura, Zitang Sun, Yuiko Sakuma +3Large Language Model-Guided NAS Large Language Model-Guided Optimization
Sony Group Corporation, Tokyo, Japan
Efficient Search of Implantable Adaptive Cells for Medical Image Segmentation
Apr 16, 2026Emil Benedykciuk, Marcin Denkowski, Grzegorz M. WójcikImage Segmentation U-Net
Institute of Computer Science and Mathematics, Maria Curie Sklodowska University, Akademicka 9, Lublin, 20-033,, Poland
Agentic Discovery with Active Hypothesis Exploration for Visual Recognition
Apr 14, 2026Jaywon Koo, Jefferson Hernandez, Ruozhen He +3Neural Architecture Search AI Agents for Scientific Discovery
Rice University
INCRT: An Incremental Transformer That Determines Its Own Architecture
Apr 12, 2026Giansalvo CirrincioneTransformer Neural Architecture Search
Laboratoire LTI, Université de Picardie Jules Verne, Amiens, France
SatReg: Regression-based Neural Architecture Search for Lightweight Satellite Image Segmentation
Apr 11, 2026Edward Humes, Tinoosh MohseninHardware-Aware NAS Neural Architecture Search
Johns Hopkins University, Baltimore, Maryland, USA
Structured Progressive Knowledge Activation for LLM-Driven Neural Architecture Search
Apr 10, 2026Zhen Liu, Yuhan Liu, Jinjun Wang +3Large Language Model-Guided NAS Large Language Model-Guided Optimization
State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University. · Zhongguancun Academy, Beijing, China. · MiLM Plus, Xiaomi Inc. +1
DDCL-INCRT: A Self-Organising Transformer with Hierarchical Prototype Structure (Theoretical Foundations)
Apr 2, 2026Giansalvo CirrincioneTransformer Hierarchical Representation Learning
Laboratory LTI, Université de Picardie Jules Verne, Chemin du Thil, Amiens, 80025, France
G-ICSO-NAS: Shifting Gears between Gradient and Swarm for Robust Neural Architecture Search
Apr 1, 2026Xingbang Du, Enzhi Zhang, Rui Zhong +2Neural Architecture Search Particle Swarm Optimization
Information Systems Design Laboratory, Hokkaido University, Sapporo, Japan · Information Initiative Center, Hokkaido University, Sapporo, Japan
ASI-Evolve: AI Accelerates AI
Mar 31, 2026Weixian Xu, Tiantian Mi, Yixiu Liu +6Autonomous Research Agents Automated Algorithm Discovery
SJTU · SII · GAIR
What an Autonomous Agent Discovers About Molecular Transformer Design: Does It Transfer?
Mar 30, 2026Edward WijayaNeural Architecture Search Transfer Learning
StemRIM, Inc.
NERO-Net: A Neuroevolutionary Approach for the Design of Adversarially Robust CNNs
Mar 26, 2026Inês Valentim, Nuno Antunes, Nuno LourençoAdversarial Robustness Convolutional Neural Networks
University of Coimbra, CISUC/LASI, DEI, Coimbra, Portugal
Holistic Scaling Laws for Optimal Mixture-of-Experts Architecture Optimization
Mar 23, 2026Weilin Wan, Jingtao Han, Debing Zhang +2Mixture-of-Experts Models AI Scaling Laws
College of Computer Science and Artificial Intelligence, Fudan University · Hi Lab, Xiaohongshu Inc. · Project Numina +1
AgenticRS-EnsNAS: Ensemble-Decoupled Self-Evolving Architecture Search
Mar 20, 2026Yun Chen, Moyu Zhang, Jinxin Hu +2Neural Architecture Search Ensemble Learning
Alibaba Group, Beijing, China
Dual-Domain Representation Alignment: Bridging 2D and 3D Vision via Geometry-Aware Architecture Search
Mar 20, 2026Haoyu Zhang, Zhihao Yu, Rui Wang +3Neural Architecture Search Hardware-Aware NAS
Hangzhou Normal University, Hangzhou, P.R. China · Trustworthy and General AI Lab, School of Engineering, Westlake University, Hangzhou, P.R. China · Department of Data Science and Artificial Intelligence, and the Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR, China +1
Fast and Generalizable NeRF Architecture Selection for Satellite Scene Reconstruction
Mar 18, 2026Devjyoti Chakraborty, Zaki Sukma, Rakandhiya D. Rachmanto +6Neural Architecture Performance Prediction Neural Radiance Fields
School of Computing, University of Georgia · Department of Geography, University of Georgia
MobileLLM-Flash: Latency-Guided On-Device LLM Design for Industry Scale Deployment
Mar 16, 2026Hanxian Huang, Igor Fedorov, Andrey Gromov +14On-Device Language Model Inference LLM Inference Acceleration
Meta AI
Auto Researching, not hyperparameter tuning: Convergence Analysis of 10,000 Experiments
Mar 16, 2026Xiaoyi LiLarge Language Model-Guided NAS Autonomous Experimentation

May 12, 2026J/K move · Enter open · S save

Zero-shot Quantum Neural Architecture Search

Tung Dao, Son N. Tran, Huynh Thi Thanh Binh

Hanoi University of Science and Technology, Hanoi, Vietnam · Deakin University, Melbourne, Australia

Quantum Machine Learning Neural Architecture Search Variational Quantum Algorithms Quantum Architecture Search

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Variational Quantum Algorithms (VQAs) are a leading approach to exploiting near-term quantum hardware, leveraging parameterized quantum circuits and classical optimization to achieve advantage. Despite their promise, the practical deployment of VQAs is challenged by the difficulty of designing quantum circuit architectures that balance expressivity, trainability, and hardware constraints. Existing evolutionary-based quantum neural architecture search methods address these challenges but suffer from high computational costs due to repeated training of candidate circuits. In this work, we identify a setting in which the Gram matrix of the Quantum Neural Tangent Kernel converges. Building on this observation, we design a zero-shot surrogate model to estimate candidate performance without full training, significantly accelerating the architecture search process. Using this surrogate, we propose MZeQAS, a Monte Carlo Tree Search (MCTS)-based Zero-Shot Quantum Neural Architecture Search framework for VQAs. By integrating proxy-based performance estimation with MCTS exploration, MZeQAS efficiently discovers high-performing architectures. Experimental results demonstrate that MZeQAS outperforms existing approaches in terms of both search efficiency and solution quality, providing a scalable and effective framework for advancing VQA deployment on noisy intermediate-scale quantum devices.