AntennaFlow is a three‑stage generative flow model that jointly addresses costly phase acquisition and offset‑mounting violations in near‑field to far‑field antenna testing. It learns an offset‑invariant embedding of amplitude fields, transports offset amplitudes to a center‑aligned field, and applies a simplified extrapolation technique valid only for centered fields. Experiments demonstrate fast, phaseless, offset‑vector‑free NF–FF reconstruction from sparse amplitude‑only measurements, outperforming existing baselines while preserving physical consistency.
By Yongzhi Li, Chongting Shen, Menglin Chen, Xun Jiang, Zhengpeng Wang
arXiv:2609.16380v1 Announce Type: new
Abstract: Class-balanced learning and label noise create a coupled failure mode: frequency correction prevents majority classes from dominating the decision rule...
By Mushir Akhtar, Akarsh J., M. Tanveer, Mohd. Arshad
arXiv:2609.16551v1 Announce Type: new
Abstract: Self-supervised learning (SSL) can reduce the need for labelled medical images, but the choice of pretext objective remains unclear for lung ultrasound...
By Moein Heidari, Junbo Rao, Jai Choraria, Wenjin Chen, David J. Foran, Ilker Hacihaliloglu
The paper "LLM Inference in a Flash!" proposes an integer‑only quantization scheme and a dictionary‑based KV cache compression technique to enable large language model inference on compute‑in‑flash (CIF) devices. By eliminating floating‑point operations and reducing KV cache traffic through sparse dictionary coding, the authors achieve minimal accuracy loss while cutting dynamic KV cache traffic by 15× on Llama‑3.1‑8B and Qwen‑2.5‑7B models.
By Sebastian Zhao, Minseo Kim, Coleman Hooper, Luca Manolache, Michael W. Mahoney, Yakun Sophia Shao, Kurt Keutzer, Amir Gholami
arXiv:2604.18616v2 Announce Type: replace-cross
Abstract: LLM coding agents can generate correct GPU kernels, but their performance still trails expert libraries. Reaching peak throughput requires co...
By Haohui Mai, Xiaoyan Guo, Xiangyun Ding, Daifeng Li, Qiuchu Yu, Chenzhun Guo, Cong Wang, Jiacheng Zhao, Christos Kozyrakis, Binhang Yuan
arXiv:2609.16073v1 Announce Type: cross
Abstract: Retrieval-Augmented Generation (RAG) serves as the primary memory architecture for long-horizon autonomous agents. However, treating shared memory as...
By Hamed HaddadPajouh, Amir AmiriTabat
The paper explores whether CLIP embeddings can detect AI-generated images by using a frozen CLIP model to extract visual embeddings and training lightweight classifiers on top. On the CIFAKE benchmark, the approach achieves 95% accuracy without language reasoning, and 85% accuracy after few-shot adaptation with 20% of the data. Certain image types, such as wide-angle photographs and oil paintings, remain challenging, highlighting unexplored difficulties in AI-generated image classification.
By Ziyang Ou
arXiv:2603.16365v3 Announce Type: replace
Abstract: We study alpha factor mining, the automated discovery of predictive signals from noisy, non-stationary market data-under a practical requirement th...
By Qinhong Lin, Ruitao Feng, Yinglun Feng, Zhenxin Huang, Yukun Chen, Zhongliang Yang, Linna Zhou, Binjie Fei, Jiaqi Liu, Yu Li
The paper introduces a framework that uses Large Language Models (LLMs) to generate synthetic time‑series data for manufacturing processes. By fine‑tuning pre‑trained LLMs on manufacturing instructions and applying Retrieval Augmented Generation (RAG), the method enhances data diversity and realism. Evaluation against traditional models such as ARIMA and LSTMs shows that the LLM‑driven approach produces higher‑quality synthetic data, better capturing temporal dependencies and improving downstream anomaly detection performance.
By Mantek Singh, Jeshwanth Challagundla, Prateek Karnal, Gagan Ganapathy, Vineet Shah, Ridam Arora
The paper introduces ORDER, a task‑conditioned retrieval‑augmented generation framework that dynamically adapts both indexing and retrieval strategies to each incoming query. It first clusters questions to learn cluster‑specific chunking, metadata filtering, and reranking settings, then routes queries to the appropriate pre‑built index via nearest‑centroid assignment. Additionally, a supervised query router predicts relevant collections and a Uniform Multi‑source Sampler distributes the retrieval budget evenly across selected sources, yielding superior performance on heterogeneous historical archives compared to existing RAG systems.
By Aur\'elien Pellet (LRE), Julien Perez, Marie Puren
arXiv:2609.17029v1 Announce Type: cross
Abstract: Traditional energy forecasting solutions rely on task-specific supervision and energy asset representations, limiting transferability and the ability...
By Liana Toderean, Tudor Cioara, Vasilis Michalakopoulos, Efstathios Sarantinopoulos, Ionut Anghel, Elissaios Sarmas
arXiv:2604.02535v2 Announce Type: replace
Abstract: Dimensionality reduction (DR) involves two longstanding trade-offs. First, preserving local neighborhoods can come at the cost of global structure....
By Zeyang Huang, Angelos Chatzimparmpas, Thomas H\"ollt, Takanori Fujiwara
arXiv:2601.11716v2 Announce Type: replace-cross
Abstract: Accurate and efficient detector simulation is essential for modern collider experiments. To reduce the high computational cost, various fast...
By Thorsten Buss, Henry Day-Hall, Frank Gaede, Gregor Kasieczka, Katja Kr\"uger
arXiv:2609.16564v1 Announce Type: new
Abstract: Retrieval-augmented generation (RAG) pipelines may omit a source's material relationship to the query. We study a pre-generation triage layer that trea...
By Kainan Zhou (Google LLC), Gangzhen Qian (Google LLC), Chuhong Xu (Sony Corporate of America), Lu Yi (Google LLC)
arXiv:2609.16874v1 Announce Type: new
Abstract: Beyond model inference, the decoding stage, which converts raw network outputs into task-level representations, constitutes a significant portion of th...
By Carmelo Scribano, Filippo Muzzini, Nedyalko Prisadnikov, Mohammad Mahdi, Yuqian Fu, Giorgia Franchini, Danda Pani Paudel, Marko Bertogna, Luc Van Gool
arXiv:2609.15997v1 Announce Type: cross
Abstract: Improving safety at intersections requires identifying crash mechanisms and recommending appropriate countermeasures. However, this process tradition...
By Abu Saif Md Nasim Uddin, Mohamed Abdel-Aty, Zubayer Islam, Parvez Anowar, Chenzhu Wang
arXiv:2609.16498v1 Announce Type: cross
Abstract: Research data repositories are essential infrastructure for scientific inquiry and for ensuring that datasets follow FAIR (Findable, Accessible, Inte...
By Daniel Ebanks, Devika Jain
arXiv:2609. 17435v1 Announce Type: new Abstract: We submit M\'eTRON-FR, a 125M GPT-2 pretrained on 92.
By Adam Zachary Wasserman, David Beauchemin
arXiv:2609.16309v1 Announce Type: new
Abstract: Despite the rapid progress of LLM-based agents for planning, code generation, and debugging, their practical value for tabular machine learning remains...
By Renat Sergazinov, Artem Chistyakov, Sergey Pankevich, Artem Babenko
RAG-CT is a defense mechanism designed to protect Retrieval-Augmented Generation (RAG) systems from leaking personally identifiable information (PII). It works by detecting malicious queries through analysis of entropy and margin distributions, applying a score-based detection method. Experiments across four attack strategies and four baseline defenses on two datasets show that RAG-CT significantly reduces PII leakage while outperforming existing defenses, all without altering the underlying LLM or retriever.
By Xingyu Lyu, Jiayimei Wang, Jianfeng He, Ning Wang, Yidan Hu, Yimin Chen