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1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Neural Fourier Surrogates for Data Reuploading Quantum Neural Networks
Sep 30, 2026Oliver Knitter, Jonathan Mei, Sang Hyub Kim +3Quantum Neural Networks Quantum Machine Learning
IonQ Inc., 4505 Campus Drive, College Park, MD 20740, USA.
Trust Is Not a Score: Runtime Assurance Contracts for High-Risk AI Agents
Sep 30, 2026Serhii ZabolotniiRuntime Enforcement Contracts
Cherkasy State Business College, Cherkasy, Ukraine · State Scientific Research Institute of Armament and Military Equipment Testing and Certification, Cherkasy, Ukraine · healthPrecision, New York, NY, USA
Beyond Token Importance: Preserving Spatial Scaffolds for Efficient Vision-Language-Action Inference
Sep 29, 2026Jiayu Chen, Shuyong Gao, Jingkai Jia +6Visual Token Pruning Diffusion-Based Vision-Language-Actions
Fudan University · The Hong Kong Polytechnic University
Rethinking Visual Token Compression for Video Large Language Models: A Simple Yet Strong Baseline
Sep 28, 2026Xiao Zhang, Wang Zeng, Sheng Jin +3Token Compression Strong Baseline
School of Computer Science and Engineering, Central South University · SenseTime Research and Tetras.AI
Can Classical Semantic-Extractive Summarization Be Evaluated in Hindi? A Replication Study
Sep 24, 2026Showket Ahmad Khan, Mudasir Mohd, Nasrullah Sheikh +4Summarization Hindi
Department of Computer Science, South Campus, University of Kashmir, Anantnag, India · IBM Research, San Jose, CA, USA · Manipal University Jaipur, Dehmi Kalan, Jaipur 303007, Rajasthan, India
Reachability-Based Capability Confinement for LLM Agents under Indirect Prompt Injection
Aug 30, 2026Wujie Xiong, Rabimba Karanjai, Yang Lu +2LLM Defense Mechanisms Large Language Model Agents
Kent State Univeristy · PayPal AI Labs · University of Houston
Business Truth, not SQL Accuracy: A Rule-Gated 7B Analytics Agent Outperforms a Direct-Prompted 32B Baseline
Aug 10, 2026Morris LeeData Science Agents Answer
Independent Researcher
Leak It: Per-Document Extraction Beyond Aggregate Membership Inference
Jul 31, 2026Victor MaricatoLeakage Disclosures
Karolinska Institutet, Sweden
Measuring the Dependency Gap: Diagnosing Inter-Column Fidelity in Tabular Generative Models
Jul 20, 2026Jie ZhangSynthetic Tabular Data Generative Architectures
Accenture, Tokyo 107-8672, Japan
MADA-RL: Multi-Agent Debate-Aware Reinforcement Learning for Parameter-Efficient Reasoning in Compact Models
Jul 20, 2026Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov +3Large Language Model Reinforcement Learning Icr-Rl
Bosch Center for Artificial Intelligence, Germany · LMU Munich, Germany · University of Oslo, Norway +2
The Model Knows Your Project, Not You: Measuring Recognition in LLMs with NameRank
Jul 14, 2026Bojie Li, Noah ShiCredibility Frontier Models
Pine AI · University of Washington
How Query Visibility Changes KV-Cache Compression Rankings: A Matched-Budget Audit
Jul 11, 2026Daming Luo, Christy Liang, Junyu XuanKey-Value Cache Compression Data Compression Methods
University of Technology Sydney
Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates
Jul 5, 2026Hao Hu, Xue-shan AiTime Series Forecasting Time Series
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China · Hubei Key Laboratory of Water System Science for Sponge City Construction (Wuhan University), Wuhan 430072, China
Rethinking AI-Generated Text Detection: A Strong Baseline and the Distribution-Shift Problem That Remains
Jul 4, 2026Zhuoer Shen, Mingyi Wang, Shaofeng Zou +1Machine-Generated Text Detection Bert-Based Models
Department of Computer Science, University of California, Santa Barbara · School of ECEE, Arizona State University
A Methodology for Investigating AI Patterns Prevalence in Software Repositories
Jul 1, 2026Srinath Perera, Hasinthaka Piyumal, Frank Leymann +1Code Quality Methodology
WSO2 · Santa Clara, CA, USA · University of Stuttgart2 +1
Comparing Human Gaze and Vision-Language Model Attention in Safety-Relevant Environments
Jun 13, 2026Marta Vallejo, Siwen WangRecent Vision-Language Models Visual Perception
School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom
DroneDAR: Long-Range Drone Distance Estimation Using Monocular Vision and Bounding-Box Features
Jun 5, 2026Knut Peterson, Zaid Mayers, David HanMonocular Bounding Box
MaPLe Research Lab, Drexel University, Philadelphia, PA
COMPOSE: Composing Future Theorems from Citations and Formal Structure
May 28, 2026David Busbib, Michael WermanFormalization Citations
Hebrew University of Jerusalem
Training-Free Fine-Grained Semantic Segmentations in Low Data Regimes: A FungiTastic Baseline
May 21, 2026Sebastian Cavada, Francesco Pelosin, Lapo FaggiFew-Shot Segmentation Semantic Segmentation
Covision Lab, Bressanone, South Tyrol, Italy
Text-to-CAD Retrieval: a Strong Baseline
May 7, 2026Honghu Pan, Zibo Du, Daxiang Liu +2Image-To-Cad Generation Cross-Modal
School of Artificial Intelligence and Robotics, Hunan University · College of Computer Science and Software Engineer, Shenzhen University · Faculty of Science and Technology, University of Macau
Efficient-VLN: A Simple yet Strong Baseline for Efficient Vision-Language Navigation
Dec 11, 2025Duo Zheng, Shijia Huang, Yanyang Li +1Vision-Language Navigation Navigation
The Chinese University of Hong Kong · Weitu AI
Semantic Correspondence: Unified Benchmarking and a Strong Baseline
May 23, 2025Kaiyan Zhang, Xinghui Li, Jingyi Lu +1Ground-Truth Correspondence Computer Vision
University of Hong Kong, Hong Kong · Department of Engineering, University of Oxford, OX1 3PJ Oxford, U.K.

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Neural Fourier Surrogates for Data Reuploading Quantum Neural Networks

Oliver Knitter, Jonathan Mei, Sang Hyub Kim, Chi Chen+2

IonQ Inc., 4505 Campus Drive, College Park, MD 20740, USA.

Quantum Neural Networks Quantum Machine Learning Strong Baseline

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For quantum machine learning, the exact boundary between classical and quantum advantage is still poorly understood. Direct comparison between quantum neural networks (QNNs) and existing classical models, which encompass fundamentally different function classes, often fails to provide broader insight into the difference between the two. Inspired by the techniques of Neural Quantum States and Random Fourier Features, this work introduces Neural Fourier Surrogates (NFS), a stochastic classical neural network architecture for efficiently learning coefficients over the same finite Fourier series support as quantum neural networks. Testing on a selection of tabular benchmark datasets, we find that NFS is an effective classifier architecture broadly competitive with established classical baselines, including a comparable Random Fourier Features model, and possessing comparable performance to data-reuploading QNNs; combined with additional analysis comparing the learned Fourier spectra of QNNs and NFS on synthetic data, these results establish NFS as a natural classical baseline for evaluating QNN performance.

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