Tootfinder

Warum Augmented Reality auf der Stelle tritt
Was hält Augmented Reality zurück? Der AR-Pionier Kharis O'Connell hat der Technologie den Rücken gekehrt und spricht offen über seine Desillusion.
https://www…

Apple zur Vision Pro: Weiter "Volle Konzentration" auf Augmented Reality
Nach wie vor kostet Apples erstes Headset mindestens 4000 Euro. Offenbar werkelt der Konzern nur noch an der Software.

Designs from magic-augmented Clifford circuits
Yuzhen Zhang, Sagar Vijay, Yingfei Gu, Yimu Bao
https://arxiv.org/abs/2507.02828 https://

Designs from magic-augmented Clifford circuits
We introduce magic-augmented Clifford circuits -- architectures in which Clifford circuits are preceded and/or followed by constant-depth circuits of non-Clifford (``magic") gates -- as a resource-efficient way to realize approximate $k$-designs, with reduced circuit depth and usage of magic. We prove that shallow Clifford circuits, when augmented with constant-depth circuits of magic gates, can generate approximate unitary and state $k$-designs with $ε$ relative error. The total circuit depth…

Bias Correction in Factor-Augmented Regression Models with Weak Factors
Peiyun Jiang, Yoshimasa Uematsu, Takashi Yamagata
https://arxiv.org/abs/2509.02066 https://

Bias Correction in Factor-Augmented Regression Models with Weak Factors
In this paper, we study the asymptotic bias of the factor-augmented regression estimator and its reduction, which is augmented by the $r$ factors extracted from a large number of $N$ variables with $T$ observations. In particular, we consider general weak latent factor models with $r$ signal eigenvalues that may diverge at different rates, $N^{α_{k}}$, $0<α_{k}\leq 1$, $k=1,\dots,r$. In the existing literature, the bias has been derived using an approximation for the estimated factors with a …

Augmented Shuffle Differential Privacy Protocols for Large-Domain Categorical and Key-Value Data
Takao Murakami, Yuichi Sei, Reo Eriguchi
https://arxiv.org/abs/2509.02004 https:…

Augmented Shuffle Differential Privacy Protocols for Large-Domain Categorical and Key-Value Data
Shuffle DP (Differential Privacy) protocols provide high accuracy and privacy by introducing a shuffler who randomly shuffles data in a distributed system. However, most shuffle DP protocols are vulnerable to two attacks: collusion attacks by the data collector and users and data poisoning attacks. A recent study addresses this issue by introducing an augmented shuffle DP protocol, where users do not add noise and the shuffler performs random sampling and dummy data addition. However, it focuse…

Finding My Way: Influence of Different Audio Augmented Reality Navigation Cues on User Experience and Subjective Usefulness
Sina Hinzmann, Francesco Vona, Juliane Henning, Mohamed Amer, Omar Abdellatif, Tanja Kojic, Jan-Niklas Voigt-Antons
https://arxiv.org/abs/2509.03199

Finding My Way: Influence of Different Audio Augmented Reality Navigation Cues on User Experience and Subjective Usefulness
As augmented reality (AR) becomes increasingly prevalent in mobile and context-aware applications, the role of auditory cues in guiding users through physical environments is becoming critical. This study investigates the effectiveness and user experience of various categories of audio cues, including fully non-verbal sounds and speech-derived Spearcons, during outdoor navigation tasks using the Meta Quest 3 headset. Twenty participants navigated five outdoor routes using audio-only cue types: …

ReCode: Improving LLM-based Code Repair with Fine-Grained Retrieval-Augmented Generation
Yicong Zhao, Shisong Chen, Jiacheng Zhang, Zhixu Li
https://arxiv.org/abs/2509.02330 htt…

ReCode: Improving LLM-based Code Repair with Fine-Grained Retrieval-Augmented Generation
Recent advances in large language models (LLMs) have demonstrated impressive capabilities in code-related tasks, such as code generation and automated program repair. Despite their promising performance, most existing approaches for code repair suffer from high training costs or computationally expensive inference. Retrieval-augmented generation (RAG), with its efficient in-context learning paradigm, offers a more scalable alternative. However, conventional retrieval strategies, which are often…

RS-OOD: A Vision-Language Augmented Framework for Out-of-Distribution Detection in Remote Sensing
Yingrui Ji, Jiansheng Chen, Jingbo Chen, Anzhi Yue, Chenhao Wang, Kai Li, Yao Zhu
https://arxiv.org/abs/2509.02273

RS-OOD: A Vision-Language Augmented Framework for Out-of-Distribution Detection in Remote Sensing
Out-of-distribution (OOD) detection represents a critical challenge in remote sensing applications, where reliable identification of novel or anomalous patterns is essential for autonomous monitoring, disaster response, and environmental assessment. Despite remarkable progress in OOD detection for natural images, existing methods and benchmarks remain poorly suited to remote sensing imagery due to data scarcity, complex multi-scale scene structures, and pronounced distribution shifts. To this e…

Optimizing Health Coverage in Ethiopia: A Learning-augmented Approach and Persistent Proportionality Under an Online Budget
Davin Choo, Yohai Trabelsi, Fentabil Getnet, Samson Warkaye Lamma, Wondesen Nigatu, Kasahun Sime, Lisa Matay, Milind Tambe, St\'ephane Verguet
https://arxiv.org/abs/2509.00135…

Optimizing Health Coverage in Ethiopia: A Learning-augmented Approach and Persistent Proportionality Under an Online Budget
As part of nationwide efforts aligned with the United Nations' Sustainable Development Goal 3 on Universal Health Coverage, Ethiopia's Ministry of Health is strengthening health posts to expand access to essential healthcare services. However, only a fraction of this health system strengthening effort can be implemented each year due to limited budgets and other competing priorities, thus the need for an optimization framework to guide prioritization across the regions of Ethiopia. In this pape…

Better by Comparison: Retrieval-Augmented Contrastive Reasoning for Automatic Prompt Optimization
Juhyeon Lee, Wonduk Seo, Hyunjin An, Seunghyun Lee, Yi Bu
https://arxiv.org/abs/2509.02093

Better by Comparison: Retrieval-Augmented Contrastive Reasoning for Automatic Prompt Optimization
Automatic prompt optimization has recently emerged as a strategy for improving the quality of prompts used in Large Language Models (LLMs), with the goal of generating more accurate and useful responses. However, most prior work focuses on direct prompt refinement or model fine-tuning, overlooking the potential of leveraging LLMs' inherent reasoning capability to learn from contrasting examples. In this paper, we present Contrastive Reasoning Prompt Optimization (CRPO), a novel framework that f…

MMRAG-DocQA: A Multi-Modal Retrieval-Augmented Generation Method for Document Question-Answering with Hierarchical Index and Multi-Granularity Retrieval
Ziyu Gong, Yihua Huang, Chengcheng Mai
https://arxiv.org/abs/2508.00579

MMRAG-DocQA: A Multi-Modal Retrieval-Augmented Generation Method for Document Question-Answering with Hierarchical Index and Multi-Granularity Retrieval
The multi-modal long-context document question-answering task aims to locate and integrate multi-modal evidences (such as texts, tables, charts, images, and layouts) distributed across multiple pages, for question understanding and answer generation. The existing methods can be categorized into Large Vision-Language Model (LVLM)-based and Retrieval-Augmented Generation (RAG)-based methods. However, the former were susceptible to hallucinations, while the latter struggled for inter-modal disconn…

RAG-PRISM: A Personalized, Rapid, and Immersive Skill Mastery Framework with Adaptive Retrieval-Augmented Tutoring
Gaurangi Raul, Yu-Zheng Lin, Karan Patel, Bono Po-Jen Shih, Matthew W. Redondo, Banafsheh Saber Latibari, Jesus Pacheco, Soheil Salehi, Pratik Satam
https://arxiv.org/abs/2509.00646

RAG-PRISM: A Personalized, Rapid, and Immersive Skill Mastery Framework with Adaptive Retrieval-Augmented Tutoring
The rapid digital transformation of Fourth Industrial Revolution (4IR) systems is reshaping workforce needs, widening skill gaps, especially for older workers. With growing emphasis on STEM skills such as robotics, automation, artificial intelligence (AI), and security, large-scale re-skilling and up-skilling are required. Training programs must address diverse backgrounds, learning styles, and motivations to improve persistence and success, while ensuring rapid, cost-effective workforce develo…

MARS: Modality-Aligned Retrieval for Sequence Augmented CTR Prediction
Yutian Xiao, Shukuan Wang, Binhao Wang, Zhao Zhang, Yanze Zhang, Shanqi Liu, Chao Feng, Xiang Li, Fuzhen Zhuang
https://arxiv.org/abs/2509.01184

MARS: Modality-Aligned Retrieval for Sequence Augmented CTR Prediction
Click-through rate (CTR) prediction serves as a cornerstone of recommender systems. Despite the strong performance of current CTR models based on user behavior modeling, they are still severely limited by interaction sparsity, especially in low-active user scenarios. To address this issue, data augmentation of user behavior is a promising research direction. However, existing data augmentation methods heavily rely on collaborative signals while overlooking the rich multimodal features of items,…

KG-RAG: Enhancing GUI Agent Decision-Making via Knowledge Graph-Driven Retrieval-Augmented Generation
Ziyi Guan, Jason Chun Lok Li, Zhijian Hou, Pingping Zhang, Donglai Xu, Yuzhi Zhao, Mengyang Wu, Jinpeng Chen, Thanh-Toan Nguyen, Pengfei Xian, Wenao Ma, Shengchao Qin, Graziano Chesi, Ngai Wong
https://arxiv.org/abs/2509.00366

KG-RAG: Enhancing GUI Agent Decision-Making via Knowledge Graph-Driven Retrieval-Augmented Generation
Despite recent progress, Graphic User Interface (GUI) agents powered by Large Language Models (LLMs) struggle with complex mobile tasks due to limited app-specific knowledge. While UI Transition Graphs (UTGs) offer structured navigation representations, they are underutilized due to poor extraction and inefficient integration. We introduce KG-RAG, a Knowledge Graph-driven Retrieval-Augmented Generation framework that transforms fragmented UTGs into structured vector databases for efficient real…

CabinSep: IR-Augmented Mask-Based MVDR for Real-Time In-Car Speech Separation with Distributed Heterogeneous Arrays
Runduo Han, Yanxin Hu, Yihui Fu, Zihan Zhang, Yukai Jv, Li Chen, Lei Xie
https://arxiv.org/abs/2509.01399

CabinSep: IR-Augmented Mask-Based MVDR for Real-Time In-Car Speech Separation with Distributed Heterogeneous Arrays
Separating overlapping speech from multiple speakers is crucial for effective human-vehicle interaction. This paper proposes CabinSep, a lightweight neural mask-based minimum variance distortionless response (MVDR) speech separation approach, to reduce speech recognition errors in back-end automatic speech recognition (ASR) models. Our contributions are threefold: First, we utilize channel information to extract spatial features, which improves the estimation of speech and noise masks. Second, …

AI-Augmented OTDR Fault Localization Framework for Resilient Rural Fiber Networks in the United States
Sabab Al Farabi (Department of Industrial Engineering, Lamar University, Beaumont, Texas, USA)
https://arxiv.org/abs/2506.03041

AI-Augmented OTDR Fault Localization Framework for Resilient Rural Fiber Networks in the United States
This research presents a novel framework that combines traditional Optical Time-Domain Reflectometer (OTDR) signal analysis with machine learning to localize and classify fiber optic faults in rural broadband infrastructures. The proposed system addresses a critical need in the expansion of middle-mile and last-mile networks, particularly in regions targeted by the U.S. Broadband Equity, Access, and Deployment (BEAD) Program. By enhancing fault diagnosis through a predictive, AI-based model, th…

Demonstrating Visual Information Manipulation Attacks in Augmented Reality: A Hands-On Miniature City-Based Setup
Yanming Xiu, Maria Gorlatova
https://arxiv.org/abs/2509.02933 h…

Demonstrating Visual Information Manipulation Attacks in Augmented Reality: A Hands-On Miniature City-Based Setup
Augmented reality (AR) enhances user interaction with the real world but also presents vulnerabilities, particularly through Visual Information Manipulation (VIM) attacks. These attacks alter important real-world visual cues, leading to user confusion and misdirected actions. In this demo, we present a hands-on experience using a miniature city setup, where users interact with manipulated AR content via the Meta Quest 3. The demo highlights the impact of VIM attacks on user decision-making and …

Power Enhancement of Permutation-Augmented Partial-Correlation Tests via Fixed-Row Permutations
Tianyi Wang, Guanghui Wang, Zhaojun Wang, Changliang Zou
https://arxiv.org/abs/2506.02906

Power Enhancement of Permutation-Augmented Partial-Correlation Tests via Fixed-Row Permutations
Permutation-based partial-correlation tests guarantee finite-sample Type I error control under any fixed design and exchangeable noise, yet their power can collapse when the permutation-augmented design aligns too closely with the covariate of interest. We remedy this by fixing a design-driven subset of rows and permuting only the remainder. The fixed rows are chosen by a greedy algorithm that maximizes a lower bound on power. This strategy reduces covariate-permutation collinearity while prese…

An Automated Attack Investigation Approach Leveraging Threat-Knowledge-Augmented Large Language Models
Rujie Dai, Peizhuo Lv, Yujiang Gui, Qiujian Lv, Yuanyuan Qiao, Yan Wang, Degang Sun, Weiqing Huang, Yingjiu Li, XiaoFeng Wang
https://arxiv.org/abs/2509.01271

An Automated Attack Investigation Approach Leveraging Threat-Knowledge-Augmented Large Language Models
Advanced Persistent Threats (APTs) are prolonged, stealthy intrusions by skilled adversaries that compromise high-value systems to steal data or disrupt operations. Reconstructing complete attack chains from massive, heterogeneous logs is essential for effective attack investigation, yet existing methods suffer from poor platform generality, limited generalization to evolving tactics, and an inability to produce analyst-ready reports. Large Language Models (LLMs) offer strong semantic understan…

Identifying Origins of Place Names via Retrieval Augmented Generation
Alexis Horde Vo, Matt Duckham, Estrid He, Rafe Benli
https://arxiv.org/abs/2509.01030 https://

Identifying Origins of Place Names via Retrieval Augmented Generation
Who is the "Batman" behind "Batman Street" in Melbourne? Understanding the historical, cultural, and societal narratives behind place names can reveal the rich context that has shaped a community. Although place names serve as essential spatial references in gazetteers, they often lack information about place name origins. Enriching these place names in today's gazetteers is a time-consuming, manual process that requires extensive exploration of a vast archive of documents and text sources. Rec…

Question Decomposition for Retrieval-Augmented Generation
Paul J. L. Ammann, Jonas Golde, Alan Akbik
https://arxiv.org/abs/2507.00355 https://

Question Decomposition for Retrieval-Augmented Generation
Grounding large language models (LLMs) in verifiable external sources is a well-established strategy for generating reliable answers. Retrieval-augmented generation (RAG) is one such approach, particularly effective for tasks like question answering: it retrieves passages that are semantically related to the question and then conditions the model on this evidence. However, multi-hop questions, such as "Which company among NVIDIA, Apple, and Google made the biggest profit in 2023?," challenge RA…

MotionRAG-Diff: A Retrieval-Augmented Diffusion Framework for Long-Term Music-to-Dance Generation
Mingyang Huang, Peng Zhang, Bang Zhang
https://arxiv.org/abs/2506.02661

MotionRAG-Diff: A Retrieval-Augmented Diffusion Framework for Long-Term Music-to-Dance Generation
Generating long-term, coherent, and realistic music-conditioned dance sequences remains a challenging task in human motion synthesis. Existing approaches exhibit critical limitations: motion graph methods rely on fixed template libraries, restricting creative generation; diffusion models, while capable of producing novel motions, often lack temporal coherence and musical alignment. To address these challenges, we propose $\textbf{MotionRAG-Diff}$, a hybrid framework that integrates Retrieval-Au…

Augmented Physics-Based Li-ion Battery Model via Adaptive Ensemble Sparse Learning and Conformal Prediction
Samuel Filgueira da Silva, Mehmet Fatih Ozkan, Faissal El Idrissi, Marcello Canova
https://arxiv.org/abs/2507.00353

Augmented Physics-Based Li-ion Battery Model via Adaptive Ensemble Sparse Learning and Conformal Prediction
Accurate electrochemical models are essential for the safe and efficient operation of lithium-ion batteries in real-world applications such as electrified vehicles and grid storage. Reduced-order models (ROM) offer a balance between fidelity and computational efficiency but often struggle to capture complex and nonlinear behaviors, such as the dynamics in the cell voltage response under high C-rate conditions. To address these limitations, this study proposes an Adaptive Ensemble Sparse Identif…

MobileRAG: A Fast, Memory-Efficient, and Energy-Efficient Method for On-Device RAG
Taehwan Park, Geonho Lee, Min-Soo Kim
https://arxiv.org/abs/2507.01079 h…

MobileRAG: A Fast, Memory-Efficient, and Energy-Efficient Method for On-Device RAG
Retrieval-Augmented Generation (RAG) has proven effective on server infrastructures, but its application on mobile devices is still underexplored due to limited memory and power resources. Existing vector search and RAG solutions largely assume abundant computation resources, making them impractical for on-device scenarios. In this paper, we propose MobileRAG, a fully on-device pipeline that overcomes these limitations by combining a mobile-friendly vector search algorithm, \textit{EcoVector}, …

Crosslisted article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[2/2]:
- AdaSwitch: An Adaptive Switching Meta-Algorithm for Learning-Augmented Bounded-Influence Problems
Xi Chen, Yuze Chen, Yuan Zhou

VR- und KI-Brillen sind nur Brücken: Der heimliche Star heißt Augmented Reality
Während VR lange Zeit lautstark um Aufmerksamkeit buhlte, schleicht sich AR in unser Leben, um zu bleiben – auf dem Rücken von Mixed Reality und Smart Glasses.

Strategyproof Mechanisms for Facility Location with Prediction Under the Maximum Cost Objective
Hau Chan, Jianan Lin, Chenhao Wang
https://arxiv.org/abs/2509.00439 https://

Strategyproof Mechanisms for Facility Location with Prediction Under the Maximum Cost Objective
We study the mechanism design problem of facility location on a metric space in the learning-augmented framework, where mechanisms have access to an imperfect prediction of optimal facility locations. Our goal is to design strategyproof (SP) mechanisms to elicit agent preferences on the facility locations truthfully and, leveraging the given imperfect prediction, determine the facility location that approximately minimizes the maximum cost among all agents. In particular, we seek SP mechanisms …

Assessing Workers Neuro-physiological Stress Responses to Augmented Reality Safety Warnings in Immersive Virtual Roadway Work Zones
Fatemeh Banani Ardecani, Omidreza Shoghli
https://arxiv.org/abs/2506.03113

Assessing Workers Neuro-physiological Stress Responses to Augmented Reality Safety Warnings in Immersive Virtual Roadway Work Zones
This paper presents a multi-stage experimental framework that integrates immersive Virtual Reality (VR) simulations, wearable sensors, and advanced signal processing to investigate construction workers neuro-physiological stress responses to multi-sensory AR-enabled warnings. Participants performed light- and moderate-intensity roadway maintenance tasks within a high-fidelity VR roadway work zone, while key stress markers of electrodermal activity (EDA), heart rate variability (HRV), and electr…

Generalized Golub-Kahan bidiagonalization for generalized saddle point systems
Na-Na Wang, Ji-Cheng Li
https://arxiv.org/abs/2509.02936 https://arxiv.org/p…

Generalized Golub-Kahan bidiagonalization for generalized saddle point systems
We consider the iterative solution of generalized saddle point systems. When the right bottom block is zero, Arioli [SIAM J. Matrix Anal. Appl., 34 (2013), pp. 571--592] proposed a CRAIG algorithm based on generalized Golub-Kahan Bidiagonalization (GKB) for the augmented systems with the leading block being symmetric and positive definite (SPD), and then Dumitrasc et al. [SIAM J. Matrix Anal. Appl., 46 (2025), pp. 370--392] extended the GKB for the case where the symmetry condition of the leadi…

MedSAM-CA: A CNN-Augmented ViT with Attention-Enhanced Multi-Scale Fusion for Medical Image Segmentation
Peiting Tian, Xi Chen, Haixia Bi, Fan Li
https://arxiv.org/abs/2506.23700 …

MedSAM-CA: A CNN-Augmented ViT with Attention-Enhanced Multi-Scale Fusion for Medical Image Segmentation
Medical image segmentation plays a crucial role in clinical diagnosis and treatment planning, where accurate boundary delineation is essential for precise lesion localization, organ identification, and quantitative assessment. In recent years, deep learning-based methods have significantly advanced segmentation accuracy. However, two major challenges remain. First, the performance of these methods heavily relies on large-scale annotated datasets, which are often difficult to obtain in medical s…

MODE: Mixture of Document Experts for RAG
Rahul Anand
https://arxiv.org/abs/2509.00100 https://arxiv.org/pdf/2509.00100

MODE: Mixture of Document Experts for RAG
Retrieval-Augmented Generation (RAG) often relies on large vector databases and cross-encoders tuned for large-scale corpora, which can be excessive for small, domain-specific collections. We present MODE (Mixture of Document Experts), a lightweight alternative that replaces fine-grained nearest-neighbor search with cluster-and-route retrieval. Documents are embedded, grouped into semantically coherent clusters, and represented by cached centroids. At query time, we route to the top centroid(s)…

High-fidelity electronic structure and properties of InSb: $G_0W_0$ and Bayesian-optimized hybrid functionals and DFT $U$ approaches
Ritwik Das, Anne-Sophie Grimault-Jacquin, Fr\'ed\'eric Aniel
https://arxiv.org/abs/2508.00290

High-fidelity electronic structure and properties of InSb: $G_0W_0$ and Bayesian-optimized hybrid functionals and DFT+$U$ approaches
This study presents a refined approach to computing the electronic structure of indium antimonide (InSb) using advanced \textit{ab initio} techniques with the In and Sb $4d^{10}$ semicore electrons included in the valence states. These states are modeled using fully relativistic projector augmented waves (PAW) and optimized norm-conserving Vanderbilt (ONCV) pseudopotentials. However, standard Kohn-Sham density-functional theory (DFT) calculations with these pseudopotentials often produce non-ph…

This https://arxiv.org/abs/2502.09891 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs) for solving question-answer (QA) tasks. The state-of-the-art RAG approaches often use the graph data as the external data since they capture the rich semantic information and link relationships between entities. However, existing graph-based RAG approaches cannot accurately identify the relevant information from the graph and also consume large numbers of tokens in the o…

HyperGaussians: High-Dimensional Gaussian Splatting for High-Fidelity Animatable Face Avatars
Gent Serifi, Marcel C. B\"uhler
https://arxiv.org/abs/2507.02803

HyperGaussians: High-Dimensional Gaussian Splatting for High-Fidelity Animatable Face Avatars
We introduce HyperGaussians, a novel extension of 3D Gaussian Splatting for high-quality animatable face avatars. Creating such detailed face avatars from videos is a challenging problem and has numerous applications in augmented and virtual reality. While tremendous successes have been achieved for static faces, animatable avatars from monocular videos still fall in the uncanny valley. The de facto standard, 3D Gaussian Splatting (3DGS), represents a face through a collection of 3D Gaussian pr…

TADA: Improved Diffusion Sampling with Training-free Augmented Dynamics
Tianrong Chen, Huangjie Zheng, David Berthelot, Jiatao Gu, Josh Susskind, Shuangfei Zhai
https://arxiv.org/abs/2506.21757

TADA: Improved Diffusion Sampling with Training-free Augmented Dynamics
Diffusion models have demonstrated exceptional capabilities in generating high-fidelity images but typically suffer from inefficient sampling. Many solver designs and noise scheduling strategies have been proposed to dramatically improve sampling speeds. In this paper, we introduce a new sampling method that is up to $186\%$ faster than the current state of the art solver for comparative FID on ImageNet512. This new sampling method is training-free and uses an ordinary differential equation (OD…

Model-Driven Quantum Code Generation Using Large Language Models and Retrieval-Augmented Generation
Nazanin Siavash, Armin Moin
https://arxiv.org/abs/2508.21097 https://

Model-Driven Quantum Code Generation Using Large Language Models and Retrieval-Augmented Generation
This paper introduces a novel research direction for model-to-text/code transformations by leveraging Large Language Models (LLMs) that can be enhanced with Retrieval-Augmented Generation (RAG) pipelines. The focus is on quantum and hybrid quantum-classical software systems, where model-driven approaches can help reduce the costs and mitigate the risks associated with the heterogeneous platform landscape and lack of developers' skills. We validate one of the proposed ideas regarding generating …

Chiral anomaly-induced nonlinear Hall effect in spin-orbit coupled noncentrosymmetric metals
Gautham Varma K, Mohd. Hashim Raza, Azaz Ahmad
https://arxiv.org/abs/2508.00821 http…

Chiral anomaly-induced nonlinear Hall effect in spin-orbit coupled noncentrosymmetric metals
Recent studies have shown that chiral anomaly is not limited to WSMs, but are also shown by a larger class of materials called spin orbit coupled noncentrosymmetric metals (SOC-NCMs),which has shed more insight into the origin of chiral anomaly as a Fermi surface property rather than a nodal property. In this study, we explore nonlinear transport responses in SOC-NCMswithin the framework of semiclassical dynamics, employing the Maxwell-Boltzmann transport theory augmented by charge conservation…

Exploring Trade Openness and Logistics Efficiency in the G20 Economies: A Bootstrap ARDL Analysis of Growth Dynamics
Haibo Wang, Lutfu Sua
https://arxiv.org/abs/2509.00368 https…

Exploring Trade Openness and Logistics Efficiency in the G20 Economies: A Bootstrap ARDL Analysis of Growth Dynamics
This study examines the relationship between trade openness, logistics performance, and economic growth within G20 economies. Using a Bootstrap Autoregressive Distributed Lag (ARDL) model augmented by a dynamic error correction mechanism (ECM), the analysis quantifies both short run and long run effects of trade facilitation and logistics infrastructure, measured via the World Bank's Logistics Performance Index (LPI) from 2007 to 2023, on economic growth. The G20, as a consortium of the world's…

Impedance Primitive-augmented Hierarchical Reinforcement Learning for Sequential Tasks
Amin Berjaoui Tahmaz, Ravi Prakash, Jens Kober
https://arxiv.org/abs/2508.19607 https://…

Impedance Primitive-augmented Hierarchical Reinforcement Learning for Sequential Tasks
This paper presents an Impedance Primitive-augmented hierarchical reinforcement learning framework for efficient robotic manipulation in sequential contact tasks. We leverage this hierarchical structure to sequentially execute behavior primitives with variable stiffness control capabilities for contact tasks. Our proposed approach relies on three key components: an action space enabling variable stiffness control, an adaptive stiffness controller for dynamic stiffness adjustments during primiti…

This https://arxiv.org/abs/2505.20552 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Effect of laboratory conditions on the perception of virtual stages for music
This manuscript presents initial findings critical for supporting augmented acoustics experiments in custom-made hearing booths, addressing a key challenge in ensuring perceptual validity and experimental rigor in these highly sensitive setups. This validation ensures our proposed methodology is sound, guarantees the reliability of future results, and lays the foundational groundwork for subsequent perceptual studies and the development of robust guidelines for laboratory design in virtual acou…

This https://arxiv.org/abs/2505.12574 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

PoisonArena: Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) systems, widely used to improve the factual grounding of large language models (LLMs), are increasingly vulnerable to poisoning attacks, where adversaries inject manipulated content into the retriever's corpus. While prior research has predominantly focused on single-attacker settings, real-world scenarios often involve multiple, competing attackers with conflicting objectives. In this work, we introduce PoisonArena, the first benchmark to systematically stu…

CMRAG: Co-modality-based document retrieval and visual question answering
Wang Chen, Guanqiang Qi, Weikang Li, Yang Li
https://arxiv.org/abs/2509.02123 https://

CMRAG: Co-modality-based document retrieval and visual question answering
Retrieval-Augmented Generation (RAG) has become a core paradigm in document question answering tasks. However, existing methods have limitations when dealing with multimodal documents: one category of methods relies on layout analysis and text extraction, which can only utilize explicit text information and struggle to capture images or unstructured content; the other category treats document segmentation as visual input and directly passes it to visual language models (VLMs) for processing, ye…

QoS-aware State-Augmented Learnable Algorithm for Wireless Coexistence Parameter Management
Mohammad Reza Fasihi, Brian L. Mark
https://arxiv.org/abs/2506.22652

QoS-aware State-Augmented Learnable Algorithm for Wireless Coexistence Parameter Management
Efficient and fair coexistence in unlicensed spectrum is essential to support heterogeneous networks such as 5G NR-U and Wi-Fi, which often contend for shared wireless resources. We introduce a general framework for wireless Coexistence Parameter Management (CPM) based on state-augmented constrained reinforcement learning. We propose a novel algorithm, QaSAL-CPM, which incorporates state-augmentation by embedding the dual variables in the constrained optimization formulation directly into the a…

Swap Network Augmented Ans\"atze on Arbitrary Connectivity
Teodor Parella-Dilm\'e
https://arxiv.org/abs/2507.23679 https://arxiv.org/pdf/2507.2367…

Swap Network Augmented Ansätze on Arbitrary Connectivity
Efficient parametrizations of quantum states are essential for trainable hybrid classical-quantum algorithms. A key challenge in their design consists in adapting to the available qubit connectivity of the quantum processor, which limits the capacity to generate correlations between distant qubits in a resource-efficient and trainable manner. In this work we first introduce an algorithm that optimizes qubit routing for arbitrary connectivity graphs, resulting in a swap network that enables dire…

Instruction-Level Weight Shaping: A Framework for Self-Improving AI Agents
Rimom Costa
https://arxiv.org/abs/2509.00251 https://arxiv.org/pdf/2509.00251

Instruction-Level Weight Shaping: A Framework for Self-Improving AI Agents
Large language models (LLMs) are fluent but largely static after pre-training; new or shifting knowledge is typically added with retrieval-augmented generation (RAG) or fine-tuning. RAG raises latency and engineering overhead and often fails to integrate facts; prompt engineering is brittle and can conflict with prior knowledge; fine-tuning is costly and risks catastrophic forgetting. We propose Instruction-Level Weight Shaping (ILWS): curated system instructions act as external, auditable pseu…

Fine-Grained Privacy Extraction from Retrieval-Augmented Generation Systems via Knowledge Asymmetry Exploitation
Yufei Chen, Yao Wang, Haibin Zhang, Tao Gu
https://arxiv.org/abs/2507.23229

Fine-Grained Privacy Extraction from Retrieval-Augmented Generation Systems via Knowledge Asymmetry Exploitation
Retrieval-augmented generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge bases, but this advancement introduces significant privacy risks. Existing privacy attacks on RAG systems can trigger data leakage but often fail to accurately isolate knowledge-base-derived sentences within mixed responses. They also lack robustness when applied across multiple domains. This paper addresses these challenges by presenting a novel black-box attack framework that ex…

Computational adversarial risk analysis for general security games
Jose Manuel Camacho, Roi Naveiro, David Rios Insua
https://arxiv.org/abs/2506.02603 http…

Computational adversarial risk analysis for general security games
This paper provides an efficient computational scheme to handle general security games from an adversarial risk analysis perspective. Two cases in relation to single-stage and multi-stage simultaneous defend-attack games motivate our approach to general setups which uses bi-agent influence diagrams as underlying problem structure and augmented probability simulation as core computational methodology. Theoretical convergence and numerical, modeling, and implementation issues are thoroughly discu…

Augmented Lagrangian methods for infeasible convex optimization problems and diverging proximal-point algorithms
Roland Andrews, Justin Carpentier, Adrien Taylor
https://arxiv.org/abs/2506.22428

Augmented Lagrangian methods for infeasible convex optimization problems and diverging proximal-point algorithms
This work investigates the convergence behavior of augmented Lagrangian methods (ALMs) when applied to convex optimization problems that may be infeasible. ALMs are a popular class of algorithms for solving constrained optimization problems. We establish progressively stronger convergence results, ranging from basic sequence convergence to precise convergence rates, under a hierarchy of assumptions. In particular, we demonstrate that, under mild assumptions, the sequences of iterates generated …

This https://arxiv.org/abs/2505.16133 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation
Retrieval-Augmented Generation (RAG) encounters efficiency challenges when scaling to massive knowledge bases while preserving contextual relevance. We propose Hash-RAG, a framework that integrates deep hashing techniques with systematic optimizations to address these limitations. Our queries directly learn binary hash codes from knowledgebase code, eliminating intermediate feature extraction steps, and significantly reducing storage and computational overhead. Building upon this hash-based eff…

Toward Understanding Bugs in Vector Database Management Systems
Yinglin Xie, Xinyi Hou, Yanjie Zhao, Shenao Wang, Kai Chen, Haoyu Wang
https://arxiv.org/abs/2506.02617

Toward Understanding Bugs in Vector Database Management Systems
Vector database management systems (VDBMSs) play a crucial role in facilitating semantic similarity searches over high-dimensional embeddings from diverse data sources. While VDBMSs are widely used in applications such as recommendation, retrieval-augmented generation (RAG), and multimodal search, their reliability remains underexplored. Traditional database reliability models cannot be directly applied to VDBMSs because of fundamental differences in data representation, query mechanisms, and s…

MUST-RAG: MUSical Text Question Answering with Retrieval Augmented Generation
Daeyong Kwon, SeungHeon Doh, Juhan Nam
https://arxiv.org/abs/2507.23334 https://

MUST-RAG: MUSical Text Question Answering with Retrieval Augmented Generation
Recent advancements in Large language models (LLMs) have demonstrated remarkable capabilities across diverse domains. While they exhibit strong zero-shot performance on various tasks, LLMs' effectiveness in music-related applications remains limited due to the relatively small proportion of music-specific knowledge in their training data. To address this limitation, we propose MusT-RAG, a comprehensive framework based on Retrieval Augmented Generation (RAG) to adapt general-purpose LLMs for tex…

EgoTouch: On-Body Touch Input Using AR/VR Headset Cameras
Vimal Mollyn, Chris Harrison
https://arxiv.org/abs/2509.01786 https://arxiv.org/pdf/2509.01786

EgoTouch: On-Body Touch Input Using AR/VR Headset Cameras
In augmented and virtual reality (AR/VR) experiences, a user's arms and hands can provide a convenient and tactile surface for touch input. Prior work has shown on-body input to have significant speed, accuracy, and ergonomic benefits over in-air interfaces, which are common today. In this work, we demonstrate high accuracy, bare hands (i.e., no special instrumentation of the user) skin input using just an RGB camera, like those already integrated into all modern XR headsets. Our results show t…

High-Dimensional Spatial Autoregression with Latent Factors by Diversified Projections
Jiaxin Shi, Xuening Zhu, Jing Zhou, Baichen Yu, Hansheng Wang
https://arxiv.org/abs/2509.00742

High-Dimensional Spatial Autoregression with Latent Factors by Diversified Projections
We study one particular type of multivariate spatial autoregression (MSAR) model with diverging dimensions in both responses and covariates. This makes the usual MSAR models no longer applicable due to the high computational cost. To address this issue, we propose a factor-augmented spatial autoregression (FSAR) model. FSAR is a special case of MSAR but with a novel factor structure imposed on the high-dimensional random error vector. The latent factors of FSAR are assumed to be of a fixed dime…

LeakSealer: A Semisupervised Defense for LLMs Against Prompt Injection and Leakage Attacks
Francesco Panebianco, Stefano Bonfanti, Francesco Trov\`o, Michele Carminati
https://arxiv.org/abs/2508.00602 …

LeakSealer: A Semisupervised Defense for LLMs Against Prompt Injection and Leakage Attacks
The generalization capabilities of Large Language Models (LLMs) have led to their widespread deployment across various applications. However, this increased adoption has introduced several security threats, notably in the forms of jailbreaking and data leakage attacks. Additionally, Retrieval Augmented Generation (RAG), while enhancing context-awareness in LLM responses, has inadvertently introduced vulnerabilities that can result in the leakage of sensitive information. Our contributions are t…

Real-Time Emergency Vehicle Siren Detection with Efficient CNNs on Embedded Hardware
Marco Giordano, Stefano Giacomelli, Claudia Rinaldi, Fabio Graziosi
https://arxiv.org/abs/2507.01563

Real-Time Emergency Vehicle Siren Detection with Efficient CNNs on Embedded Hardware
We present a full-stack emergency vehicle (EV) siren detection system designed for real-time deployment on embedded hardware. The proposed approach is based on E2PANNs, a fine-tuned convolutional neural network derived from EPANNs, and optimized for binary sound event detection under urban acoustic conditions. A key contribution is the creation of curated and semantically structured datasets - AudioSet-EV, AudioSet-EV Augmented, and Unified-EV - developed using a custom AudioSet-Tools framework…

Robustness Enhancement for Multi-Quadrotor Centralized Transportation System via Online Tuning and Learning
Tianhua Gao, Kohji Tomita, Akiya Kamimura
https://arxiv.org/abs/2509.01952

Robustness Enhancement for Multi-Quadrotor Centralized Transportation System via Online Tuning and Learning
This paper introduces an adaptive-neuro geometric control for a centralized multi-quadrotor cooperative transportation system, which enhances both adaptivity and disturbance rejection. Our strategy is to coactively tune the model parameters and learn the external disturbances in real-time. To realize this, we augmented the existing geometric control with multiple neural networks and adaptive laws, where the estimated model parameters and the weights of the neural networks are simultaneously tun…

Systemic Trade Risk Suppresses Comparative Advantage in Rare Earth Dependent Industries
Peter Klimek, Sophia Baum, Markus Gerschberger, Maximilian Hess
https://arxiv.org/abs/2508.00556

Systemic Trade Risk Suppresses Comparative Advantage in Rare Earth Dependent Industries
Rare earth elements (REEs) are critical to a wide range of clean and high-tech applications, yet global trade dependencies expose countries to vulnerabilities across production networks. Here, we construct a multi-tiered input-output trade network spanning 168 REE-related product codes from 2007-2023 using a novel AI-augmented statistical framework. We identify significant differences between dependencies in upstream and intermediate (input) products, revealing that exposure and supplier concen…

Agentic large language models improve retrieval-based radiology question answering
Sebastian Wind, Jeta Sopa, Daniel Truhn, Mahshad Lotfinia, Tri-Thien Nguyen, Keno Bressem, Lisa Adams, Mirabela Rusu, Harald K\"ostler, Gerhard Wellein, Andreas Maier, Soroosh Tayebi Arasteh
https://arxiv.org/abs/2508.00743

Agentic large language models improve retrieval-based radiology question answering
Clinical decision-making in radiology increasingly benefits from artificial intelligence (AI), particularly through large language models (LLMs). However, traditional retrieval-augmented generation (RAG) systems for radiology question answering (QA) typically rely on single-step retrieval, limiting their ability to handle complex clinical reasoning tasks. Here we propose an agentic RAG framework enabling LLMs to autonomously decompose radiology questions, iteratively retrieve targeted clinical …

Unlocking Mixed Reality for Medical Education: A See-Through Perspective on Head Anatomy
Yuqing Wei, Yupeng Wang, Jiayi Zhao, Yanjun Liu, Huxin Gao, Jiewen Lai
https://arxiv.org/abs/2509.00352

Unlocking Mixed Reality for Medical Education: A See-Through Perspective on Head Anatomy
Extended reality (XR), encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), is emerging as a transformative platform for medical education. Traditional methods such as textbooks, physical models, and cadaveric dissections often lack interactivity and fail to convey complex spatial relationships effectively. The emerging MR technology addresses these limitations by providing immersive environments that blend virtual elements with real-world contexts. This study pres…

The Augmented Mixing Method: Computing High-Accuracy Primal-Dual Solutions to Large-Scale SDPs via Column Updates
Daniel Brosch, Jan Schwiddessen, Angelika Wiegele
https://arxiv.org/abs/2507.20386

The Augmented Mixing Method: Computing High-Accuracy Primal-Dual Solutions to Large-Scale SDPs via Column Updates
The Burer-Monteiro factorization has become a powerful tool for solving large-scale semidefinite programs (SDPs), enabling recently developed low-rank solvers to tackle problems previously beyond reach. However, existing methods are typically designed to prioritize scalability over solution accuracy. We introduce the Augmented Mixing Method, a new algorithm that combines the Burer-Monteiro factorization with an inexact augmented Lagrangian framework and a block coordinate descent scheme. Our me…

Improving LLM Agents with Reinforcement Learning on Cryptographic CTF Challenges
Lajos Muzsai, David Imolai, Andr\'as Luk\'acs
https://arxiv.org/abs/2506.02048

Improving LLM Agents with Reinforcement Learning on Cryptographic CTF Challenges
Large Language Models (LLMs) still struggle with the structured reasoning and tool-assisted computation needed for problem solving in cybersecurity applications. In this work, we introduce "random-crypto", a cryptographic Capture-the-Flag (CTF) challenge generator framework that we use to fine-tune a tool-augmented Llama-3.1-8B with Guided Reinforcement Prompt Optimisation (GRPO), allowing the agent to iteratively write and execute Python inside an isolated REPL. GRPO yields a +53% absolute jum…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/2]:
- Retrieval-Augmented Semantic Parsing: Improving Generalization with Lexical Knowledge
Xiao Zhang, Qianru Meng, Johan Bos

MADrive: Memory-Augmented Driving Scene Modeling
Polina Karpikova, Daniil Selikhanovych, Kirill Struminsky, Ruslan Musaev, Maria Golitsyna, Dmitry Baranchuk
https://arxiv.org/abs/2506.21520

MADrive: Memory-Augmented Driving Scene Modeling
Recent advances in scene reconstruction have pushed toward highly realistic modeling of autonomous driving (AD) environments using 3D Gaussian splatting. However, the resulting reconstructions remain closely tied to the original observations and struggle to support photorealistic synthesis of significantly altered or novel driving scenarios. This work introduces MADrive, a memory-augmented reconstruction framework designed to extend the capabilities of existing scene reconstruction methods by r…

Re-examining the Legendre-Gauss-Lobatto Pseudospectral Methods for Optimal Control
Yilin Zou, Fanghua Jiang
https://arxiv.org/abs/2507.01660 https://

Re-examining the Legendre-Gauss-Lobatto Pseudospectral Methods for Optimal Control
Pseudospectral methods represent an efficient approach for solving optimal control problems. While Legendre-Gauss-Lobatto (LGL) collocation points have traditionally been considered inferior to Legendre-Gauss (LG) and Legendre-Gauss-Radau (LGR) points in terms of convergence properties, this paper presents a rigorous re-examination of LGL-based methods. We introduce an augmented formulation that enhances the standard LGL collocation approach by incorporating an additional degree of freedom (DOF…

ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation
Yuqicheng Zhu, Nico Potyka, Daniel Hern\'andez, Yuan He, Zifeng Ding, Bo Xiong, Dongzhuoran Zhou, Evgeny Kharlamov, Steffen Staab
https://arxiv.org/abs/2508.20131

ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation
Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, yet suffers from critical limitations in high-stakes domains -- namely, sensitivity to noisy or contradictory evidence and opaque, stochastic decision-making. We propose ArgRAG, an explainable, and contestable alternative that replaces black-box reasoning with structured inference using a Quantitative Bipolar Argumentation Framework (QBAF). ArgRAG constructs a QBAF from retrieved documents a…

SmartPoser: Arm Pose Estimation with a Smartphone and Smartwatch Using UWB and IMU Data
Nathan DeVrio, Vimal Mollyn, Chris Harrison
https://arxiv.org/abs/2509.03451 https://

SmartPoser: Arm Pose Estimation with a Smartphone and Smartwatch Using UWB and IMU Data
The ability to track a user's arm pose could be valuable in a wide range of applications, including fitness, rehabilitation, augmented reality input, life logging, and context-aware assistants. Unfortunately, this capability is not readily available to consumers. Systems either require cameras, which carry privacy issues, or utilize multiple worn IMUs or markers. In this work, we describe how an off-the-shelf smartphone and smartwatch can work together to accurately estimate arm pose. Moving be…

CyberRAG: An agentic RAG cyber attack classification and reporting tool
Francesco Blefari, Cristian Cosentino, Francesco Aurelio Pironti, Angelo Furfaro, Fabrizio Marozzo
https://arxiv.org/abs/2507.02424

CyberRAG: An agentic RAG cyber attack classification and reporting tool
Intrusion Detection and Prevention Systems (IDS/IPS) in large enterprises can generate hundreds of thousands of alerts per hour, overwhelming security analysts with logs that demand deep, rapidly evolving domain expertise. Conventional machine-learning detectors trim the alert volume but still yield high false-positive rates, while standard single-pass Retrieval-Augmented Generation (RAG) pipelines often retrieve irrelevant context and fail to justify their predictions. To overcome these shortc…

This https://arxiv.org/abs/2506.00261 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

GPR: Empowering Generation with Graph-Pretrained Retriever
Graph retrieval-augmented generation (GRAG) places high demands on graph-specific retrievers. However, existing retrievers often rely on language models pretrained on plain text, limiting their effectiveness due to domain misalignment and structure ignorance. To address these challenges, we propose GPR, a graph-based retriever pretrained directly on knowledge graphs. GPR aligns natural language questions with relevant subgraphs through LLM-guided graph augmentation and employs a structure-aware…

NyayaRAG: Realistic Legal Judgment Prediction with RAG under the Indian Common Law System
Shubham Kumar Nigam, Balaramamahanthi Deepak Patnaik, Shivam Mishra, Ajay Varghese Thomas, Noel Shallum, Kripabandhu Ghosh, Arnab Bhattacharya
https://arxiv.org/abs/2508.00709

NyayaRAG: Realistic Legal Judgment Prediction with RAG under the Indian Common Law System
Legal Judgment Prediction (LJP) has emerged as a key area in AI for law, aiming to automate judicial outcome forecasting and enhance interpretability in legal reasoning. While previous approaches in the Indian context have relied on internal case content such as facts, issues, and reasoning, they often overlook a core element of common law systems, which is reliance on statutory provisions and judicial precedents. In this work, we propose NyayaRAG, a Retrieval-Augmented Generation (RAG) framewo…

CyGATE: Game-Theoretic Cyber Attack-Defense Engine for Patch Strategy Optimization
Yuning Jiang, Nay Oo, Qiaoran Meng, Lu Lin, Dusit Niyato, Zehui Xiong, Hoon Wei Lim, Biplab Sikdar
https://arxiv.org/abs/2508.00478

CyGATE: Game-Theoretic Cyber Attack-Defense Engine for Patch Strategy Optimization
Modern cyber attacks unfold through multiple stages, requiring defenders to dynamically prioritize mitigations under uncertainty. While game-theoretic models capture attacker-defender interactions, existing approaches often rely on static assumptions and lack integration with real-time threat intelligence, limiting their adaptability. This paper presents CyGATE, a game-theoretic framework modeling attacker-defender interactions, using large language models (LLMs) with retrieval-augmented genera…

What's Behind the Magic? Audiences Seek Artistic Value in Generative AI's Contributions to a Live Dance Performance
Jacqueline Elise Bruen, Myounghoon Jeon
https://arxiv.org/abs/2508.00239

What's Behind the Magic? Audiences Seek Artistic Value in Generative AI's Contributions to a Live Dance Performance
With the development of generative artificial intelligence (GenAI) tools to create art, stakeholders cannot come to an agreement on the value of these works. In this study we uncovered the mixed opinions surrounding art made by AI. We developed two versions of a dance performance augmented by technology either with or without GenAI. For each version we informed audiences of the performance's development either before or after a survey on their perceptions of the performance. There were thirty-n…

ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
Reza Yousefi Maragheh, Pratheek Vadla, Priyank Gupta, Kai Zhao, Aysenur Inan, Kehui Yao, Jianpeng Xu, Praveen Kanumala, Jason Cho, Sushant Kumar
https://arxiv.org/abs/2506.21931

ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
Retrieval-Augmented Generation (RAG) has shown promise in enhancing recommendation systems by incorporating external context into large language model prompts. However, existing RAG-based approaches often rely on static retrieval heuristics and fail to capture nuanced user preferences in dynamic recommendation scenarios. In this work, we introduce ARAG, an Agentic Retrieval-Augmented Generation framework for Personalized Recommendation, which integrates a multi-agent collaboration mechanism int…

Enhancing Document VQA Models via Retrieval-Augmented Generation
Eric L\'opez, Artemis Llabr\'es, Ernest Valveny
https://arxiv.org/abs/2508.18984 https://

Enhancing Document VQA Models via Retrieval-Augmented Generation
Document Visual Question Answering (Document VQA) must cope with documents that span dozens of pages, yet leading systems still concatenate every page or rely on very large vision-language models, both of which are memory-hungry. Retrieval-Augmented Generation (RAG) offers an attractive alternative, first retrieving a concise set of relevant segments before generating answers from this selected evidence. In this paper, we systematically evaluate the impact of incorporating RAG into Document VQA…

An AST-guided LLM Approach for SVRF Code Synthesis
Abanoub E. Abdelmalak, Mohamed A. Elsayed, David Abercrombie, Ilhami Torunoglu
https://arxiv.org/abs/2507.00352

An AST-guided LLM Approach for SVRF Code Synthesis
Standard Verification Rule Format (SVRF) is essential for semiconductor applications like Design Rule Check (DRC), Layout Versus Schematic (LVS), and Optical Proximity Correction (OPC) and it faces challenges as advancing nodes create complex design rules that renders traditional SVRF development ineffective and highlight an expertise gap. This paper introduces a novel methodology integrating Abstract Syntax Tree (AST) embedding and Retrieval-Augmented Generation (RAG) for enhanced SVRF code sy…

From Sufficiency to Reflection: Reinforcement-Guided Thinking Quality in Retrieval-Augmented Reasoning for LLMs
Jie He, Victor Gutierrez Basulto, Jeff Z. Pan
https://arxiv.org/abs/2507.22716

From Sufficiency to Reflection: Reinforcement-Guided Thinking Quality in Retrieval-Augmented Reasoning for LLMs
Reinforcement learning-based retrieval-augmented generation (RAG) methods enhance the reasoning abilities of large language models (LLMs). However, most rely only on final-answer rewards, overlooking intermediate reasoning quality. This paper analyzes existing RAG reasoning models and identifies three main failure patterns: (1) information insufficiency, meaning the model fails to retrieve adequate support; (2) faulty reasoning, where logical or content-level flaws appear despite sufficient inf…

ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking
Yuzheng Cai, Yanzhao Zhang, Dingkun Long, Mingxin Li, Pengjun Xie, Weiguo Zheng
https://arxiv.org/abs/2509.00520

ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking
Text reranking models are a crucial component in modern systems like Retrieval-Augmented Generation, tasked with selecting the most relevant documents prior to generation. However, current Large Language Models (LLMs) powered rerankers often face a fundamental trade-off. On one hand, Supervised Fine-Tuning based pointwise methods that frame relevance as a binary classification task lack the necessary scoring discrimination, particularly for those built on reasoning LLMs. On the other hand, appr…

InSituTale: Enhancing Augmented Data Storytelling with Physical Objects
Kentaro Takahira, Yue Yu, Takanori Fujiwara, Suzuki Ryo, Huamin Qu
https://arxiv.org/abs/2507.21411 https…

InSituTale: Enhancing Augmented Data Storytelling with Physical Objects
Augmented data storytelling enhances narrative delivery by integrating visualizations with physical environments and presenter actions. Existing systems predominantly rely on body gestures or speech to control visualizations, leaving interactions with physical objects largely underexplored. We introduce augmented physical data storytelling, an approach enabling presenters to manipulate visualizations through physical object interactions. To inform this approach, we first conducted a survey of d…

Med-R$^3$: Enhancing Medical Retrieval-Augmented Reasoning of LLMs via Progressive Reinforcement Learning
Keer Lu, Zheng Liang, Youquan Li, Jiejun Tan, Da Pan, Shusen Zhang, Guosheng Dong, Huang Leng
https://arxiv.org/abs/2507.23541

Med-R$^3$: Enhancing Medical Retrieval-Augmented Reasoning of LLMs via Progressive Reinforcement Learning
In medical scenarios, effectively retrieving external knowledge and leveraging it for rigorous logical reasoning is of significant importance. Despite their potential, existing work has predominantly focused on enhancing either retrieval or reasoning capabilities of the models in isolation, with little attention given to their joint optimization, which leads to limited coordination between the two processes. Additionally, current methods rely heavily on supervised fine-tuning (SFT), which can c…

This https://arxiv.org/abs/2411.06256 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Annotative Indexing
This paper introduces annotative indexing, a novel framework that unifies and generalizes traditional inverted indexes, column stores, object stores, and graph databases. As a result, annotative indexing can provide the underlying indexing framework for databases that support retrieval augmented generation, knowledge graphs, entity retrieval, semi-structured data, and ranked retrieval. While we primarily focus on human language data in the form of text, annotative indexing is sufficiently gener…

Focus on the Experts: Co-designing an Augmented Reality Eye-Gaze Tracking System with Surgical Trainees to Improve Endoscopic Instruction
Jumanh Atoum, Jinkyung Park, Mamtaj Akter, Nicholas Kavoussi, Pamela Wisniewski, Jie Ying Wu
https://arxiv.org/abs/2506.21896

Focus on the Experts: Co-designing an Augmented Reality Eye-Gaze Tracking System with Surgical Trainees to Improve Endoscopic Instruction
The current apprenticeship model for surgical training requires a high level of supervision, which does not scale well to meet the growing need for more surgeons. Many endoscopic procedures are directly taught in the operating room (OR) while the attending surgeon and trainee operate on patients. The need to prioritize patient care limits the trainees' opportunities to experiment and receive feedback on their performance. Augmented reality (AR) has the potential to increase efficiency in endosc…

A Data Science Approach to Calcutta High Court Judgments: An Efficient LLM and RAG-powered Framework for Summarization and Similar Cases Retrieval
Puspendu Banerjee, Aritra Mazumdar, Wazib Ansar, Saptarsi Goswami, Amlan Chakrabarti
https://arxiv.org/abs/2507.01058

A Data Science Approach to Calcutta High Court Judgments: An Efficient LLM and RAG-powered Framework for Summarization and Similar Cases Retrieval
The judiciary, as one of democracy's three pillars, is dealing with a rising amount of legal issues, needing careful use of judicial resources. This research presents a complex framework that leverages Data Science methodologies, notably Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) techniques, to improve the efficiency of analyzing Calcutta High Court verdicts. Our framework focuses on two key aspects: first, the creation of a robust summarization mechanism that distills…

RATE: An LLM-Powered Retrieval Augmented Generation Technology-Extraction Pipeline
Karan Mirhosseini, Arya Aftab, Alireza Sheikh
https://arxiv.org/abs/2507.21125 https://…

RATE: An LLM-Powered Retrieval Augmented Generation Technology-Extraction Pipeline
In an era of radical technology transformations, technology maps play a crucial role in enhancing decision making. These maps heavily rely on automated methods of technology extraction. This paper introduces Retrieval Augmented Technology Extraction (RATE), a Large Language Model (LLM) based pipeline for automated technology extraction from scientific literature. RATE combines Retrieval Augmented Generation (RAG) with multi-definition LLM-based validation. This hybrid method results in high rec…

Identification of Design Recommendations for Augmented Reality Authors in Corporate Training
Stefan Graser, Martin Schrepp, Stephan B\"ohm
https://arxiv.org/abs/2507.21722 …

Identification of Design Recommendations for Augmented Reality Authors in Corporate Training
Innovative technologies, such as Augmented Reality (AR), introduce new interaction paradigms, demanding the identification of software requirements during the software development process. In general, design recommendations are related to this, supporting the design of applications positively and meeting stakeholder needs. However, current research lacks context-specific AR design recommendations. This study addresses this gap by identifying and analyzing practical AR design recommendations rel…

MSRS: Evaluating Multi-Source Retrieval-Augmented Generation
Rohan Phanse, Yijie Zhou, Kejian Shi, Wencai Zhang, Yixin Liu, Yilun Zhao, Arman Cohan
https://arxiv.org/abs/2508.20867

MSRS: Evaluating Multi-Source Retrieval-Augmented Generation
Retrieval-augmented systems are typically evaluated in settings where information required to answer the query can be found within a single source or the answer is short-form or factoid-based. However, many real-world applications demand the ability to integrate and summarize information scattered across multiple sources, where no single source is sufficient to respond to the user's question. In such settings, the retrieval component of a RAG pipeline must recognize a variety of relevance signa…

Disabling Self-Correction in Retrieval-Augmented Generation via Stealthy Retriever Poisoning
Yanbo Dai, Zhenlan Ji, Zongjie Li, Kuan Li, Shuai Wang
https://arxiv.org/abs/2508.20083

Disabling Self-Correction in Retrieval-Augmented Generation via Stealthy Retriever Poisoning
Retrieval-Augmented Generation (RAG) has become a standard approach for improving the reliability of large language models (LLMs). Prior work demonstrates the vulnerability of RAG systems by misleading them into generating attacker-chosen outputs through poisoning the knowledge base. However, this paper uncovers that such attacks could be mitigated by the strong \textit{self-correction ability (SCA)} of modern LLMs, which can reject false context once properly configured. This SCA poses a signi…

UiS-IAI@LiveRAG: Retrieval-Augmented Information Nugget-Based Generation of Responses
Weronika {\L}ajewska, Ivica Kostric, Gabriel Iturra-Bocaz, Mariam Arustashvili, Krisztian Balog
https://arxiv.org/abs/2506.22210

UiS-IAI@LiveRAG: Retrieval-Augmented Information Nugget-Based Generation of Responses
Retrieval-augmented generation (RAG) faces challenges related to factual correctness, source attribution, and response completeness. The LiveRAG Challenge hosted at SIGIR'25 aims to advance RAG research using a fixed corpus and a shared, open-source LLM. We propose a modular pipeline that operates on information nuggets-minimal, atomic units of relevant information extracted from retrieved documents. This multistage pipeline encompasses query rewriting, passage retrieval and reranking, nugget d…

An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs
Mathieu Bourdin, Anas Neumann, Thomas Paviot, Robert Pellerin, Samir Lamouri
https://arxiv.org/abs/2508.21024

An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs
Retrieval-Augmented Generation (RAG) has emerged as a powerful solution to mitigate the limitations of Large Language Models (LLMs), such as hallucinations and outdated knowledge. However, deploying RAG-based tools in Small and Medium Enterprises (SMEs) remains a challenge due to their limited resources and lack of expertise in natural language processing (NLP). This paper introduces EASI-RAG, Enterprise Application Support for Industrial RAG, a structured, agile method designed to facilitate t…

TeamCMU at Touch\'e: Adversarial Co-Evolution for Advertisement Integration and Detection in Conversational Search
To Eun Kim, Jo\~ao Coelho, Gbemileke Onilude, Jai Singh
https://arxiv.org/abs/2507.00509

TeamCMU at Touché: Adversarial Co-Evolution for Advertisement Integration and Detection in Conversational Search
As conversational search engines increasingly adopt generation-based paradigms powered by Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), the integration of advertisements into generated responses presents both commercial opportunities and challenges for user experience. Unlike traditional search, where advertisements are clearly delineated, generative systems blur the boundary between informational content and promotional material, raising concerns around transparency an…

Machine Assistant with Reliable Knowledge: Enhancing Student Learning via RAG-based Retrieval
Yongsheng Lian
https://arxiv.org/abs/2506.23026 https://

Machine Assistant with Reliable Knowledge: Enhancing Student Learning via RAG-based Retrieval
We present Machine Assistant with Reliable Knowledge (MARK), a retrieval-augmented question-answering system designed to support student learning through accurate and contextually grounded responses. The system is built on a retrieval-augmented generation (RAG) framework, which integrates a curated knowledge base to ensure factual consistency. To enhance retrieval effectiveness across diverse question types, we implement a hybrid search strategy that combines dense vector similarity with sparse…

LFD: Layer Fused Decoding to Exploit External Knowledge in Retrieval-Augmented Generation
Yang Sun, Lixin Zou, Dan Luo, Zhiyong Xie, Long Zhang, Liming Dong, Yunwei Zhao, Xixun Lin, Yanxiong Lu, Chenliang Li
https://arxiv.org/abs/2508.19614

LFD: Layer Fused Decoding to Exploit External Knowledge in Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) incorporates external knowledge into large language models (LLMs), improving their adaptability to downstream tasks and enabling information updates. Surprisingly, recent empirical evidence demonstrates that injecting noise into retrieved relevant documents paradoxically facilitates exploitation of external knowledge and improves generation quality. Although counterintuitive and challenging to apply in practice, this phenomenon enables granular control and r…

MapAgent: Trajectory-Constructed Memory-Augmented Planning for Mobile Task Automation
Yi Kong, Dianxi Shi, Guoli Yang, Zhang ke-di, Chenlin Huang, Xiaopeng Li, Songchang Jin
https://arxiv.org/abs/2507.21953

MapAgent: Trajectory-Constructed Memory-Augmented Planning for Mobile Task Automation
The recent advancement of autonomous agents powered by Large Language Models (LLMs) has demonstrated significant potential for automating tasks on mobile devices through graphical user interfaces (GUIs). Despite initial progress, these agents still face challenges when handling complex real-world tasks. These challenges arise from a lack of knowledge about real-life mobile applications in LLM-based agents, which may lead to ineffective task planning and even cause hallucinations. To address the…

Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation
Guanting Dong, Xiaoxi Li, Yuyao Zhang, Mengjie Deng
https://arxiv.org/abs/2506.21384

Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation
Real-world live retrieval-augmented generation (RAG) systems face significant challenges when processing user queries that are often noisy, ambiguous, and contain multiple intents. While RAG enhances large language models (LLMs) with external knowledge, current systems typically struggle with such complex inputs, as they are often trained or evaluated on cleaner data. This paper introduces Omni-RAG, a novel framework designed to improve the robustness and effectiveness of RAG systems in live, o…

CAL-RAG: Retrieval-Augmented Multi-Agent Generation for Content-Aware Layout Design
Najmeh Forouzandehmehr, Reza Yousefi Maragheh, Sriram Kollipara, Kai Zhao, Topojoy Biswas, Evren Korpeoglu, Kannan Achan
https://arxiv.org/abs/2506.21934

CAL-RAG: Retrieval-Augmented Multi-Agent Generation for Content-Aware Layout Design
Automated content-aware layout generation -- the task of arranging visual elements such as text, logos, and underlays on a background canvas -- remains a fundamental yet under-explored problem in intelligent design systems. While recent advances in deep generative models and large language models (LLMs) have shown promise in structured content generation, most existing approaches lack grounding in contextual design exemplars and fall short in handling semantic alignment and visual coherence. In…

Retrieval-Augmented Generation for Natural Language Art Provenance Searches in the Getty Provenance Index
Mathew Henrickson
https://arxiv.org/abs/2508.19093 https://

Retrieval-Augmented Generation for Natural Language Art Provenance Searches in the Getty Provenance Index
This research presents a Retrieval-Augmented Generation (RAG) framework for art provenance studies, focusing on the Getty Provenance Index. Provenance research establishes the ownership history of artworks, which is essential for verifying authenticity, supporting restitution and legal claims, and understanding the cultural and historical context of art objects. The process is complicated by fragmented, multilingual archival data that hinders efficient retrieval. Current search portals require …

Education-Oriented Graph Retrieval-Augmented Generation for Learning Path Recommendation
Xinghe Cheng, Zihan Zhang, Jiapu Wang, Liangda Fang, Chaobo He, Quanlong Guan, Shirui Pan, Weiqi Luo
https://arxiv.org/abs/2506.22303

Education-Oriented Graph Retrieval-Augmented Generation for Learning Path Recommendation
Learning path recommendation seeks to provide learners with a structured sequence of learning items (e.g., knowledge concepts or exercises) to optimize their learning efficiency. Despite significant efforts in this area, most existing methods primarily rely on prerequisite relationships, which present two major limitations: 1) Many educational datasets do not explicitly provide prerequisite relationships between knowledge concepts, hindering the application of current learning path recommendati…

TAPS: Tool-Augmented Personalisation via Structured Tagging
Ekaterina Taktasheva, Jeff Dalton
https://arxiv.org/abs/2506.20409 https://

TAPS: Tool-Augmented Personalisation via Structured Tagging
Recent advancements in tool-augmented large language models have enabled them to interact with external tools, enhancing their ability to perform complex user tasks. However, existing approaches overlook the role of personalisation in guiding tool use. This work investigates how user preferences can be effectively integrated into goal-oriented dialogue agents. Through extensive analysis, we identify key weaknesses in the ability of LLMs to personalise tool use. To this end, we introduce \name, …

Controlled Retrieval-augmented Context Evaluation for Long-form RAG
Jia-Huei Ju, Suzan Verberne, Maarten de Rijke, Andrew Yates
https://arxiv.org/abs/2506.20051

Controlled Retrieval-augmented Context Evaluation for Long-form RAG
Retrieval-augmented generation (RAG) enhances large language models by incorporating context retrieved from external knowledge sources. While the effectiveness of the retrieval module is typically evaluated with relevance-based ranking metrics, such metrics may be insufficient to reflect the retrieval's impact on the final RAG result, especially in long-form generation scenarios. We argue that providing a comprehensive retrieval-augmented context is important for long-form RAG tasks like report…

SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization
Dhruv Gupta, Gayathri Ganesh Lakshmy, Yiqing Xie
https://arxiv.org/abs/2506.20081

SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization
Retrieval-Augmented Code Generation (RACG) is a critical technique for enhancing code generation by retrieving relevant information. In this work, we conduct an in-depth analysis of code retrieval by systematically masking specific features while preserving code functionality. Our discoveries include: (1) although trained on code, current retrievers heavily rely on surface-level textual features (e.g., docstrings, identifier names), and (2) they exhibit a strong bias towards well-documented cod…

Read the Docs Before Rewriting: Equip Rewriter with Domain Knowledge via Continual Pre-training
Qi Wang, Yixuan Cao, Yifan Liu, Jiangtao Zhao, Ping Luo
https://arxiv.org/abs/2507.00477

Read the Docs Before Rewriting: Equip Rewriter with Domain Knowledge via Continual Pre-training
A Retrieval-Augmented Generation (RAG)-based question-answering (QA) system enhances a large language model's knowledge by retrieving relevant documents based on user queries. Discrepancies between user queries and document phrasings often necessitate query rewriting. However, in specialized domains, the rewriter model may struggle due to limited domain-specific knowledge. To resolve this, we propose the R\&R (Read the doc before Rewriting) rewriter, which involves continual pre-training on pro…

\texttt{WebANNS}: Fast and Efficient Approximate Nearest Neighbor Search in Web Browsers
Mugeng Liu, Siqi Zhong, Qi Yang, Yudong Han, Xuanzhe Liu, Yun Ma
https://arxiv.org/abs/2507.00521

\texttt{WebANNS}: Fast and Efficient Approximate Nearest Neighbor Search in Web Browsers
Approximate nearest neighbor search (ANNS) has become vital to modern AI infrastructure, particularly in retrieval-augmented generation (RAG) applications. Numerous in-browser ANNS engines have emerged to seamlessly integrate with popular LLM-based web applications, while addressing privacy protection and challenges of heterogeneous device deployments. However, web browsers present unique challenges for ANNS, including computational limitations, external storage access issues, and memory utiliz…