Tootfinder

Zehn Jahre und 100 Milliarden Dollar später: Wo bleibt Metas Metaverse?
Meta hat über 100 Milliarden Dollar in Virtual und Augmented Reality investiert. Was hat Meta erreicht, und was bleibt Vision? Eine Bestandsaufnahme.

Vocabuild: An Accessible Augmented Tangible Interface for Gamified Vocabulary Learning of Constructing Meaning
Siying Hu, Zhenhao Zhang
https://arxiv.org/abs/2509.11027 https://…

Vocabuild: An Accessible Augmented Tangible Interface for Gamified Vocabulary Learning of Constructing Meaning
Vocabulary acquisition in early education often relies on rote memorization and passive screen-based tools, which can fail to engage students kinesthetically and collaboratively. This paper introduces Vocabuild, an augmented tangible interface designed to transform vocabulary learning into an active, embodied, and playful experience. The system combines physical letter blocks with a projection-augmented surface. As children physically construct words with the blocks, the system provides real-ti…

LASLiN: A Learning-Augmented Peer-to-Peer Network
Julien Dallot, Caio Caldeira, Arash Pourdamghani, Olga Goussevskaia, Stefan Schmid
https://arxiv.org/abs/2509.11904 https://

LASLiN: A Learning-Augmented Peer-to-Peer Network
We introduce a learning-augmented peer-to-peer (P2P) network design that leverages the predictions of traffic patterns to optimize the network's topology. While keeping formal guarantees on the standard P2P metrics (routing path length, maximum degree), we optimize the network in a demand-aware manner and minimize the path lengths weighted by the peer-to-peer communication demands. Our protocol is learning-augmented, meaning that each node receives an individual, possibly inaccurate prediction …

Probing Latent Knowledge Conflict for Faithful Retrieval-Augmented Generation
Linfeng Gao, Baolong Bi, Zheng Yuan, Le Wang, Zerui Chen, Zhimin Wei, Shenghua Liu, Qinggang Zhang, Jinsong Su
https://arxiv.org/abs/2510.12460

Probing Latent Knowledge Conflict for Faithful Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm to enhance the factuality of Large Language Models (LLMs). However, existing RAG systems often suffer from an unfaithfulness issue, where the model's response contradicts evidence from the retrieved context. Existing approaches to improving contextual faithfulness largely rely on external interventions, such as prompt engineering, decoding constraints, or reward-based fine-tuning. These works treat the LLM as a black box an…

The Role of Parametric Injection-A Systematic Study of Parametric Retrieval-Augmented Generation
Minghao Tang, Shiyu Ni, Jingtong Wu, Zengxin Han, Keping Bi
https://arxiv.org/abs/2510.12668

The Role of Parametric Injection-A Systematic Study of Parametric Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by retrieving external documents. As an emerging form of RAG, parametric retrieval-augmented generation (PRAG) encodes documents as model parameters (i.e., LoRA modules) and injects these representations into the model during inference, enabling interaction between the LLM and documents at parametric level. Compared with directly placing documents in the input context, PRAG is more efficient and has the potential to offe…

Augmented Reality-Enhanced Robot Teleoperation for Collecting User Demonstrations
Shiqi Gong, Sebastian Zudaire, Chi Zhang, Zhen Li
https://arxiv.org/abs/2509.11783 https://

Augmented Reality-Enhanced Robot Teleoperation for Collecting User Demonstrations
Traditional industrial robot programming is often complex and time-consuming, typically requiring weeks or even months of effort from expert programmers. Although Programming by Demonstration (PbD) offers a more accessible alternative, intuitive interfaces for robot control and demonstration collection remain challenging. To address this, we propose an Augmented Reality (AR)-enhanced robot teleoperation system that integrates AR-based control with spatial point cloud rendering, enabling intuiti…

Q-Linear Convergence of the Proximal Augmented Lagrangian Method for Non-Convex Conic Programming
Ning Zhang, Yi Zhang
https://arxiv.org/abs/2509.11531 https://

Q-Linear Convergence of the Proximal Augmented Lagrangian Method for Non-Convex Conic Programming
This paper provides a local convergence analysis of the proximal augmented Lagrangian method (PALM) applied to a class of non-convex conic programming problems. Previous convergence results for PALM typically imposed assumptions such as constraint non-degeneracy, strict complementarity, second-order sufficiency conditions, or a combination of constraint nondegeneracy with strong second-order sufficiency conditions. In contrast, our work demonstrates a Q-linear convergence rate for an inexact ve…

IP-Augmented Multi-Modal Malicious URL Detection Via Token-Contrastive Representation Enhancement and Multi-Granularity Fusion
Ye Tian, Yanqiu Yu, Liangliang Song, Zhiquan Liu, Yanbin Wang, Jianguo Sun
https://arxiv.org/abs/2510.12395

IP-Augmented Multi-Modal Malicious URL Detection Via Token-Contrastive Representation Enhancement and Multi-Granularity Fusion
Malicious URL detection remains a critical cybersecurity challenge as adversaries increasingly employ sophisticated evasion techniques including obfuscation, character-level perturbations, and adversarial attacks. Although pre-trained language models (PLMs) like BERT have shown potential for URL analysis tasks, three limitations persist in current implementations: (1) inability to effectively model the non-natural hierarchical structure of URLs, (2) insufficient sensitivity to character-level o…

Maximum augmented Zagreb index on polyomino chains
Manuel Montes-y-Morales, Sayl\'e Sigarreta, Hugo Cruz-Su\'arez
https://arxiv.org/abs/2509.10669 https://

Maximum augmented Zagreb index on polyomino chains
In this paper, we present a dynamic programming approach for identifying extremal polyomino chains with respect to degree-based topological indices. This approach provides an explicit recurrence and constructive algorithm that enables both the computation of an extremal polyomino chain in linear time with respect to its number of squares, and the enumeration of all maximal configurations in linear time with respect to their amount. As a main application, we resolve a problem posed in 2016 by ch…

Social Simulation for Integrating Self-Care: Measuring the Effects of Contextual Environments in Augmented Reality for Mental Health Practice
Anna Fang, Jiayang Shi, Hriday Chhabria, Bosi Li, Haiyi Zhu
https://arxiv.org/abs/2510.12081

Social Simulation for Integrating Self-Care: Measuring the Effects of Contextual Environments in Augmented Reality for Mental Health Practice
Despite growing interest in virtual and augmented reality (VR/AR) for mental well-being, prior work using immersive interventions to teach mental health skills has largely focused on calming or abstract settings. As a result, little is known about how realistic social simulation may better support the transfer and application of skills to in-person environments. In this work, we present a 14-day user study with 43-participants comparing an augmented reality intervention simulating a realistic c…

Query-Specific GNN: A Comprehensive Graph Representation Learning Method for Retrieval Augmented Generation
Yuchen Yan, Zhihua Liu, Hao Wang, Weiming Li, Xiaoshuai Hao
https://arxiv.org/abs/2510.11541 …

Query-Specific GNN: A Comprehensive Graph Representation Learning Method for Retrieval Augmented Generation
Retrieval-augmented generation (RAG) has demonstrated its ability to enhance Large Language Models (LLMs) by integrating external knowledge sources. However, multi-hop questions, which require the identification of multiple knowledge targets to form a synthesized answer, raise new challenges for RAG systems. Under the multi-hop settings, existing methods often struggle to fully understand the questions with complex semantic structures and are susceptible to irrelevant noise during the retrieval…

Forscher warnen vor visuellen Angriffen in Augmented Reality
Virtuelle Schilder, falsche Wege: Eine AR-Demo zeigt, wie gefährlich subtile Manipulation in Augmented Reality werden kann.
https://www.

PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation
Xiangjun Zai, Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Wenjie Zhang
https://arxiv.org/abs/2510.12434

PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation
Knowledge Hypergraphs (KHs) have recently emerged as a knowledge representation for retrieval-augmented generation (RAG), offering a paradigm to model multi-entity relations into a structured form. However, existing KH-based RAG methods suffer from three major limitations: static retrieval planning, non-adaptive retrieval execution, and superficial use of KH structure and semantics, which constrain their ability to perform effective multi-hop question answering. To overcome these limitations, w…

Scaling to Multimodal and Multichannel Heart Sound Classification: Fine-Tuning Wav2Vec 2.0 with Synthetic and Augmented Biosignals
Milan Marocchi, Matthew Fynn, Kayapanda Mandana, Yue Rong
https://arxiv.org/abs/2509.11606

Scaling to Multimodal and Multichannel Heart Sound Classification: Fine-Tuning Wav2Vec 2.0 with Synthetic and Augmented Biosignals
Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for approximately 17.9 million deaths each year. Early detection is critical, creating a demand for accurate and inexpensive pre-screening methods. Deep learning has recently been applied to classify abnormal heart sounds indicative of CVDs using synchronised phonocardiogram (PCG) and electrocardiogram (ECG) signals, as well as multichannel PCG (mPCG). However, state-of-the-art architectures remain underutilised…

Towards Scalable O-RAN Resource Management: Graph-Augmented Proximal Policy Optimization
Duc-Thinh Ngo (STACK, LS2N), Kandaraj Piamrat, Ons Aouedi, Thomas Hassan, Philippe Raipin-Parv\'edy
https://arxiv.org/abs/2509.10499

Towards Scalable O-RAN Resource Management: Graph-Augmented Proximal Policy Optimization
Open Radio Access Network (O-RAN) architectures enable flexible, scalable, and cost-efficient mobile networks by disaggregating and virtualizing baseband functions. However, this flexibility introduces significant challenges for resource management, requiring joint optimization of functional split selection and virtualized unit placement under dynamic demands and complex topologies. Existing solutions often address these aspects separately or lack scalability in large and real-world scenarios. …

RAG-Anything: All-in-One RAG Framework
Zirui Guo, Xubin Ren, Lingrui Xu, Jiahao Zhang, Chao Huang
https://arxiv.org/abs/2510.12323 https://arxiv.org/pdf/25…

RAG-Anything: All-in-One RAG Framework
Retrieval-Augmented Generation (RAG) has emerged as a fundamental paradigm for expanding Large Language Models beyond their static training limitations. However, a critical misalignment exists between current RAG capabilities and real-world information environments. Modern knowledge repositories are inherently multimodal, containing rich combinations of textual content, visual elements, structured tables, and mathematical expressions. Yet existing RAG frameworks are limited to textual content, …

PanoTPS-Net: Panoramic Room Layout Estimation via Thin Plate Spline Transformation
Hatem Ibrahem, Ahmed Salem, Qinmin Vivian Hu, Guanghui Wang
https://arxiv.org/abs/2510.11992 h…

PanoTPS-Net: Panoramic Room Layout Estimation via Thin Plate Spline Transformation
Accurately estimating the 3D layout of rooms is a crucial task in computer vision, with potential applications in robotics, augmented reality, and interior design. This paper proposes a novel model, PanoTPS-Net, to estimate room layout from a single panorama image. Leveraging a Convolutional Neural Network (CNN) and incorporating a Thin Plate Spline (TPS) spatial transformation, the architecture of PanoTPS-Net is divided into two stages: First, a convolutional neural network extracts the high-l…

RAG4Tickets: AI-Powered Ticket Resolution via Retrieval-Augmented Generation on JIRA and GitHub Data
Mohammad Baqar
https://arxiv.org/abs/2510.08667 https://

RAG4Tickets: AI-Powered Ticket Resolution via Retrieval-Augmented Generation on JIRA and GitHub Data
Modern software teams frequently encounter delays in resolving recurring or related issues due to fragmented knowledge scattered across JIRA tickets, developer discussions, and GitHub pull requests (PRs). To address this challenge, we propose a Retrieval-Augmented Generation (RAG) framework that integrates Sentence-Transformers for semantic embeddings with FAISS-based vector search to deliver context-aware ticket resolution recommendations. The approach embeds historical JIRA tickets, user comm…

Lowering Barriers to CAD Adoption: A Comparative Study of Augmented Reality-Based CAD (AR-CAD) and a Traditional CAD tool
Muhammad Talha, Abdullah Mohiuddin, Sehrish Javed, Ahmed Jawad Qureshi
https://arxiv.org/abs/2510.12146

Lowering Barriers to CAD Adoption: A Comparative Study of Augmented Reality-Based CAD (AR-CAD) and a Traditional CAD tool
The paper presents a comparative user study between an Augmented Reality-based Computer-Aided Design (AR-CAD) system and a traditional computer-based CAD modeling software, SolidWorks. Twenty participants of varying skill levels performed 3D modeling tasks using both systems. The results showed that while the average task completion time is comparable for both groups, novice designers had a higher completion rate in AR-CAD than in the traditional CAD interface, and experienced designers had a s…

Warum haben diese ganzen Militärunternehmen eigentlich immer so elbische Namen?
https://social.heise.de/@heiseonline/115372421331730114

Observation of quantum-field-theory dynamics on a spin-phonon quantum computer
Anton T. Than, Saurabh V. Kadam, Vinay Vikramaditya, Nhung H. Nguyen, Xingxin Liu, Zohreh Davoudi, Alaina M. Green, Norbert M. Linke
https://arxiv.org/abs/2509.11477

Observation of quantum-field-theory dynamics on a spin-phonon quantum computer
Simulating out-of-equilibrium dynamics of quantum field theories in nature is challenging with classical methods, but is a promising application for quantum computers. Unfortunately, simulating interacting bosonic fields involves a high boson-to-qubit encoding overhead. Furthermore, when mapping to qubits, the infinite-dimensional Hilbert space of bosons is necessarily truncated, with truncation errors that grow with energy and time. A qubit-based quantum computer, augmented with an active boso…

Uncertainty Quantification for Retrieval-Augmented Reasoning
Heydar Soudani, Hamed Zamani, Faegheh Hasibi
https://arxiv.org/abs/2510.11483 https://arxiv.or…

Uncertainty Quantification for Retrieval-Augmented Reasoning
Retrieval-augmented reasoning (RAR) is a recent evolution of retrieval-augmented generation (RAG) that employs multiple reasoning steps for retrieval and generation. While effective for some complex queries, RAR remains vulnerable to errors and misleading outputs. Uncertainty quantification (UQ) offers methods to estimate the confidence of systems' outputs. These methods, however, often handle simple queries with no retrieval or single-step retrieval, without properly handling RAR setup. Accura…

Gene-R1: Reasoning with Data-Augmented Lightweight LLMs for Gene Set Analysis
Zhizheng Wang, Yifan Yang, Qiao Jin, Zhiyong Lu
https://arxiv.org/abs/2509.10575 https://

Gene-R1: Reasoning with Data-Augmented Lightweight LLMs for Gene Set Analysis
The gene set analysis (GSA) is a foundational approach for uncovering the molecular functions associated with a group of genes. Recently, LLM-powered methods have emerged to annotate gene sets with biological functions together with coherent explanatory insights. However, existing studies primarily focus on proprietary models, which have been shown to outperform their open-source counterparts despite concerns over cost and data privacy. Furthermore, no research has investigated the application …

A GPU-Accelerated RAG-Based Telegram Assistant for Supporting Parallel Processing Students
Guy Tel-Zur
https://arxiv.org/abs/2509.11947 https://arxiv.org/p…

A GPU-Accelerated RAG-Based Telegram Assistant for Supporting Parallel Processing Students
This project addresses a critical pedagogical need: offering students continuous, on-demand academic assistance beyond conventional reception hours. I present a domain-specific Retrieval-Augmented Generation (RAG) system powered by a quantized Mistral-7B Instruct model and deployed as a Telegram bot. The assistant enhances learning by delivering real-time, personalized responses aligned with the "Introduction to Parallel Processing" course materials. GPU acceleration significantly improves infe…

Dual Shellability of Admissible Set and Cohen-Macaulayness of Local Models
Xuhua He, Qingchao Yu
https://arxiv.org/abs/2509.11581 https://arxiv.org/pdf/250…

Dual Shellability of Admissible Set and Cohen-Macaulayness of Local Models
We prove Görtz's combinatorial conjecture \cite{Go01} on dual shellability of admissible sets in Iwahori-Weyl groups, proving that the augmented admissible set $\widehat{\mathrm{Adm}}(μ)$ is dual shellable for any dominant coweight $μ$. This provides a uniform, elementary approach to establishing Cohen-Macaulayness of the special fibers of the local models with Iwahori level structure for all reductive groups-including residue characteristic $2$ and non-reduced root systems-circumventing geo…

Anduril zeigt erste Bilder des AR-Militärhelms "EagleEye"
Anduril Industries will US-Soldaten mithilfe von Augmented Reality Superkräfte verleihen. Nun zeigt das Rüstungsunternehmen erste Bilder zweier Prototypen.

Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation
Jiaying Wu, Zihang Fu, Haonan Wang, Fanxiao Li, Min-Yen Kan
https://arxiv.org/abs/2510.11423 …

Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation
Community Notes, the crowd-sourced misinformation governance system on X (formerly Twitter), enables users to flag misleading posts, attach contextual notes, and vote on their helpfulness. However, our analysis of 30.8K health-related notes reveals significant latency, with a median delay of 17.6 hours before the first note receives a helpfulness status. To improve responsiveness during real-world misinformation surges, we propose CrowdNotes+, a unified framework that leverages large language m…

FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval
Ying Li, Mengyu Wang, Miguel de Carvalho, Sotirios Sabanis, Tiejun Ma
https://arxiv.org/abs/2509.12042 https://

FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval
Financial disclosures such as 10-K filings present challenging retrieval problems due to their length, regulatory section hierarchy, and domain-specific language, which standard retrieval-augmented generation (RAG) models underuse. We introduce FinGEAR (Financial Mapping-Guided Enhanced Answer Retrieval), a retrieval framework tailored to financial documents. FinGEAR combines a finance lexicon for Item-level guidance (FLAM), dual hierarchical indices for within-Item search (Summary Tree and Que…

Replaced article(s) found for cs.DS. https://arxiv.org/list/cs.DS/new
[1/1]:
- Decision-Theoretic Approaches for Improved Learning-Augmented Algorithms
Spyros Angelopoulos, Christoph D\"urr, Georgii Melidi

Exploring the Null Results in the Direct Detection Experiments and the $\mathbf{(g-2)_\ell}$ in an Extended $\mathbf{U(1)_{L_\mu-L_\tau}}$ Model Constrained through the $\mathbf{Z\to\ell^ \ell^-}$ Decays
Bibhabasu De
https://arxiv.org/abs/2509.10939

Exploring the Null Results in the Direct Detection Experiments and the $\mathbf{(g-2)_\ell}$ in an Extended $\mathbf{U(1)_{L_μ-L_τ}}$ Model Constrained through the $\mathbf{Z\to\ell^+\ell^-}$ Decays
The Direct Detection (DD) experiments are vital for probing the particle nature of the Dark Matter (DM). However, in the absence of a scattering event, DD searches result in stringent bounds on the corresponding parameter space. The paper has considered a $U(1)_{L_μ-L_τ}$-extension of the Standard Model (SM) and augmented the particle spectrum with $SU(2)_L$-singlet vector-like leptons and scalars. Further, a discrete $Z_2$ symmetry stabilizes the lightest SM-singlet vector-like lepton as the…

Audio-Maestro: Enhancing Large Audio-Language Models with Tool-Augmented Reasoning
Kuan-Yi Lee, Tsung-En Lin, Hung-Yi Lee
https://arxiv.org/abs/2510.11454 https://

Audio-Maestro: Enhancing Large Audio-Language Models with Tool-Augmented Reasoning
Recent advancements in large multimodal models (LMMs) have shown strong capabilities in audio understanding. However, most systems rely solely on end-to-end reasoning, limiting interpretability and accuracy for tasks that require structured knowledge or specialized signal analysis. In this work, we present Audio-Maestro -- a tool-augmented audio reasoning framework that enables audio-language models to autonomously call external tools and integrate their timestamped outputs into the reasoning p…

FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation
Samuel Hildebrand (Louisiana State University), Curtis Taylor (Oak Ridge National Lab), Sean Oesch (Oak Ridge National Lab), James M Ghawaly Jr (Louisiana State University), Amir Sadovnik (Oak Ridge National Lab), Ryan Shivers (Oak Ridge National Lab), Brandon Schreiber (Oak Ridge National Lab), Kevin Kurian (University of Florida)

FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation
Retrieval-augmented generation (RAG) has emerged as a promising paradigm for improving factual accuracy in large language models (LLMs). We introduce a benchmark designed to evaluate RAG pipelines as a whole, evaluating a pipeline's ability to ingest, retrieve, and reason about several modalities of information, differentiating it from existing benchmarks that focus on particular aspects such as retrieval. We present (1) a small, human-created dataset of 93 questions designed to evaluate a pipe…

LLM-Specific Utility: A New Perspective for Retrieval-Augmented Generation
Hengran Zhang, Keping Bi, Jiafeng Guo, Jiaming Zhang, Shuaiqiang Wang, Dawei Yin, Xueqi Cheng
https://arxiv.org/abs/2510.11358

LLM-Specific Utility: A New Perspective for Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge. While traditional retrieval focuses on relevance, RAG's effectiveness depends on the utility of retrieved passages, i.e., the usefulness in facilitating the generation of an accurate and comprehensive answer. Existing studies often treat utility as a generic attribute, ignoring the fact that different LLMs may benefit differently from the same passage due to variations in internal kno…

Replaced article(s) found for cs.GR. https://arxiv.org/list/cs.GR/new
[1/1]:
- Augmented Mass-Spring model for Real-Time Dense Hair Simulation
Herrera, Zhou, Sun, Shu, He, Pirk, Michels
…

Seeing like Meta: Smart glasses and the ethics of augmented reality #AR

Seeing like Meta: Smart glasses and the ethics of augmented reality - ABC Religion & Ethics
Meta’s smart glasses are not just another consumer gadget; they are a step toward enclosing perception itself within a private platform — and when the interface is your glasses, there is no “outside”.

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[9/14]:
- Hyper-STTN: Hypergraph Augmented Spatial-Temporal Transformer Network for Trajectory Prediction
Weizheng Wang, Baijian Yang, Sungeun Hong, Wenhai Sun, Byung-Cheol Min

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/5]:
- J-RAS: Enhancing Medical Image Segmentation via Retrieval-Augmented Joint Training
Salma J. Ahmed, Emad A. Mohammed, Azam Asilian Bidgoli

Augmented data and neural networks for robust epidemic forecasting: application to COVID-19 in Italy
Giacomo Dimarco, Federica Ferrarese, Lorenzo Pareschi
https://arxiv.org/abs/2510.09192

Augmented data and neural networks for robust epidemic forecasting: application to COVID-19 in Italy
In this work, we propose a data augmentation strategy aimed at improving the training phase of neural networks and, consequently, the accuracy of their predictions. Our approach relies on generating synthetic data through a suitable compartmental model combined with the incorporation of uncertainty. The available data are then used to calibrate the model, which is further integrated with deep learning techniques to produce additional synthetic data for training. The results show that neural net…

Spatially-Augmented Sequence-to-Sequence Neural Diarization for Meetings
Li Li, Ming Cheng, Hongyu Zhang, Juan Liu, Ming Li
https://arxiv.org/abs/2510.09505 https://

Spatially-Augmented Sequence-to-Sequence Neural Diarization for Meetings
This paper proposes a Spatially-Augmented Sequence-to-Sequence Neural Diarization (SA-S2SND) framework, which integrates direction-of-arrival (DOA) cues estimated by SRP-DNN into the S2SND backbone. A two-stage training strategy is adopted: the model is first trained with single-channel audio and DOA features, and then further optimized with multi-channel inputs under DOA guidance. In addition, a simulated DOA generation scheme is introduced to alleviate dependence on matched multi-channel corp…

Learning Robust Agile Flight Control with Stability Guarantees
Lukas Pries, Markus Ryll
https://arxiv.org/abs/2510.12611 https://arxiv.org/pdf/2510.12611…

Learning Robust Agile Flight Control with Stability Guarantees
In the evolving landscape of high-speed agile quadrotor flight, achieving precise trajectory tracking at the platform's operational limits is paramount. Controllers must handle actuator constraints, exhibit robustness to disturbances, and remain computationally efficient for safety-critical applications. In this work, we present a novel neural-augmented feedback controller for agile flight control. The controller addresses individual limitations of existing state-of-the-art control paradigms an…

VeriCite: Towards Reliable Citations in Retrieval-Augmented Generation via Rigorous Verification
Haosheng Qian, Yixing Fan, Jiafeng Guo, Ruqing Zhang, Qi Chen, Dawei Yin, Xueqi Cheng
https://arxiv.org/abs/2510.11394

VeriCite: Towards Reliable Citations in Retrieval-Augmented Generation via Rigorous Verification
Retrieval-Augmented Generation (RAG) has emerged as a crucial approach for enhancing the responses of large language models (LLMs) with external knowledge sources. Despite the impressive performance in complex question-answering tasks, RAG still struggles with hallucinations. Attributing RAG-generated content through in-line citations has demonstrated potential in reducing hallucinations and facilitating human verification. Existing citation generation methods primarily rely on either fine-tuni…

Diffusion-Augmented Reinforcement Learning for Robust Portfolio Optimization under Stress Scenarios
Himanshu Choudhary, Arishi Orra, Manoj Thakur
https://arxiv.org/abs/2510.07099

Diffusion-Augmented Reinforcement Learning for Robust Portfolio Optimization under Stress Scenarios
In the ever-changing and intricate landscape of financial markets, portfolio optimisation remains a formidable challenge for investors and asset managers. Conventional methods often struggle to capture the complex dynamics of market behaviour and align with diverse investor preferences. To address this, we propose an innovative framework, termed Diffusion-Augmented Reinforcement Learning (DARL), which synergistically integrates Denoising Diffusion Probabilistic Models (DDPMs) with Deep Reinforc…

SSNCVX: A primal-dual semismooth Newton method for convex composite optimization problem
Zhanwang Deng, Tao Wei, Jirui Ma, Zaiwen Wen
https://arxiv.org/abs/2509.11995 https://…

SSNCVX: A primal-dual semismooth Newton method for convex composite optimization problem
In this paper, we propose a uniform semismooth Newton-based algorithmic framework called SSNCVX for solving a broad class of convex composite optimization problems. By exploiting the augmented Lagrangian duality, we reformulate the original problem into a saddle point problem and characterize the optimality conditions via a semismooth system of nonlinear equations. The nonsmooth structure is handled internally without requiring problem specific transformation or introducing auxiliary variable…

Zephyrus: Scaling Gateways Beyond the Petabit-Era with DPU-Augmented Hierarchical Co-Offloading
Yuemeng Xu, Haoran Chen, Jiarui Guo, Mingwei Cui, Qiuheng Yin, Cheng Dong, Daxiang Kang, Xian Wu, Chenmin Sun, Peng He, Yang Gao, Lirong Lai, Kai Wang, Hongyu Wu, Tong Yang, Xiyun Xu
https://arxiv.org/abs/2510.11043

Zephyrus: Scaling Gateways Beyond the Petabit-Era with DPU-Augmented Hierarchical Co-Offloading
Operating at petabit-scale, ByteDance's cloud gateways are deployed at critical aggregation points to orchestrate a wide array of business traffic. However, this massive scale imposes significant resource pressure on our previous-generation cloud gateways, rendering them unsustainable in the face of ever-growing cloud-network traffic. As the DPU market rapidly expands, we see a promising path to meet our escalating business traffic demands by integrating DPUs with our established Tofino-based g…

DeepMMSearch-R1: Empowering Multimodal LLMs in Multimodal Web Search
Kartik Narayan, Yang Xu, Tian Cao, Kavya Nerella, Vishal M. Patel, Navid Shiee, Peter Grasch, Chao Jia, Yinfei Yang, Zhe Gan
https://arxiv.org/abs/2510.12801

DeepMMSearch-R1: Empowering Multimodal LLMs in Multimodal Web Search
Multimodal Large Language Models (MLLMs) in real-world applications require access to external knowledge sources and must remain responsive to the dynamic and ever-changing real-world information in order to address information-seeking and knowledge-intensive user queries. Existing approaches, such as retrieval augmented generation (RAG) methods, search agents, and search equipped MLLMs, often suffer from rigid pipelines, excessive search calls, and poorly constructed search queries, which resu…

LLM Enhancement with Domain Expert Mental Model to Reduce LLM Hallucination with Causal Prompt Engineering
Boris Kovalerchuk, Brent D. Fegley
https://arxiv.org/abs/2509.10818 ht…

LLM Enhancement with Domain Expert Mental Model to Reduce LLM Hallucination with Causal Prompt Engineering
Difficult decision-making problems abound in various disciplines and domains. The proliferation of generative techniques, especially large language models (LLMs), has excited interest in using them for decision support. However, LLMs cannot yet resolve missingness in their training data, leading to hallucinations. Retrieval-Augmented Generation (RAG) enhances LLMs by incorporating external information retrieval, reducing hallucinations and improving accuracy. Yet, RAG and related methods are on…

Designing Control Barrier Functions Using a Dynamic Backup Policy
Victor Freire, Marco M. Nicotra
https://arxiv.org/abs/2510.09810 https://arxiv.org/pdf/25…

Designing Control Barrier Functions Using a Dynamic Backup Policy
This paper presents a systematic approach to construct control barrier functions for nonlinear control affine systems subject to arbitrary state and input constraints. Taking inspiration from the reference governor literature, the proposed method defines a family of backup policies, parametrized by the equilibrium manifold of the system. The control barrier function is defined on the augmented state-and-reference space: given a state-reference pair, the approach quantifies the distance to const…

#zuck #orion #ar #vr #glasses

RAG-Pull: Imperceptible Attacks on RAG Systems for Code Generation
Vasilije Stambolic, Aritra Dhar, Lukas Cavigelli
https://arxiv.org/abs/2510.11195 https://

RAG-Pull: Imperceptible Attacks on RAG Systems for Code Generation
Retrieval-Augmented Generation (RAG) increases the reliability and trustworthiness of the LLM response and reduces hallucination by eliminating the need for model retraining. It does so by adding external data into the LLM's context. We develop a new class of black-box attack, RAG-Pull, that inserts hidden UTF characters into queries or external code repositories, redirecting retrieval toward malicious code, thereby breaking the models' safety alignment. We observe that query and code perturbat…

Halpern Acceleration of the Inexact Proximal Point Method of Rockafellar
Liwei Zhang, Fanli Zhuang, Ning Zhang
https://arxiv.org/abs/2511.10372 https://arxiv.org/pdf/2511.10372 https://arxiv.org/html/2511.10372
arXiv:2511.10372v1 Announce Type: new
Abstract: This paper investigates a Halpern acceleration of the inexact proximal point method for solving maximal monotone inclusion problems in Hilbert spaces. The proposed Halpern inexact proximal point method (HiPPM) is shown to be globally convergent, and a unified framework is developed to analyze its worst-case convergence rate. Under mild summability conditions on the inexactness tolerances, HiPPM achieves an $\mathcal{O}(1/k^{2})$ rate in terms of the squared fixed-point residual. Furthermore, under additional mild condition, the method retains a fast linear convergence rate. Building upon this framework, we further extend the acceleration technique to constrained convex optimization through the augmented Lagrangian formulation. In analogy to Rockafellar's classical results, the resulting accelerated inexact augmented Lagrangian method inherits the convergence rate and complexity guarantees of HiPPM. The analysis thus provides a unified theoretical foundation for accelerated inexact proximal algorithms and their augmented Lagrangian extensions.
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Memory-Augmented Generative AI for Real-time Wireless Prediction in Dynamic Industrial Environments
Rahul Gulia, Amlan Ganguly, Michael E. Kuhl, Ehsan Rashedi, Clark Hochgraf
https://arxiv.org/abs/2510.06884

Memory-Augmented Generative AI for Real-time Wireless Prediction in Dynamic Industrial Environments
Accurate and real-time prediction of wireless channel conditions, particularly the Signal-to-Interference-plus-Noise Ratio (SINR), is a foundational requirement for enabling Ultra-Reliable Low-Latency Communication (URLLC) in highly dynamic Industry 4.0 environments. Traditional physics-based or statistical models fail to cope with the spatio-temporal complexities introduced by mobile obstacles and transient interference inherent to smart warehouses. To address this, we introduce Evo-WISVA (Evo…

A Review of 10 Years of ProtoSpace: Spacecraft CAD Visualization in Collaborative Augmented Reality
Benjamin Nuernberger, Samuel-Hunter Berndt, Robert Tapella, Laura Mann, Aaron Plave, Sasha Samochina, Victor X. Luo
https://arxiv.org/abs/2510.06608

A Review of 10 Years of ProtoSpace: Spacecraft CAD Visualization in Collaborative Augmented Reality
ProtoSpace is a custom JPL-built platform to help scientists and engineers visualize their CAD models collaboratively in augmented reality (AR) and on the web in 3D. In addition to this main use case, ProtoSpace has been used throughout the entire spacecraft mission lifecycle and beyond: ventilator design and assembly; providing AR-based instructions to astronauts in-training; educating the next generation on the process of spacecraft design; etc. ProtoSpace has been used for a decade by NASA m…

A Dynamic Knowledge Update-Driven Model with Large Language Models for Fake News Detection
Di Jin, Jun Yang, Xiaobao Wang, Junwei Zhang, Shuqi Li, Dongxiao He
https://arxiv.org/abs/2509.11687

A Dynamic Knowledge Update-Driven Model with Large Language Models for Fake News Detection
As the Internet and social media evolve rapidly, distinguishing credible news from a vast amount of complex information poses a significant challenge. Due to the suddenness and instability of news events, the authenticity labels of news can potentially shift as events develop, making it crucial for fake news detection to obtain the latest event updates. Existing methods employ retrieval-augmented generation to fill knowledge gaps, but they suffer from issues such as insufficient credibility of …

Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration
Mohanakrishnan Hariharan, Satish Arvapalli, Seshu Barma, Evangeline Sheela
https://arxiv.org/abs/2510.10824

Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration
We present an approach to software testing automation using Agentic Retrieval-Augmented Generation (RAG) systems for Quality Engineering (QE) artifact creation. We combine autonomous AI agents with hybrid vector-graph knowledge systems to automate test plan, case, and QE metric generation. Our approach addresses traditional software testing limitations by leveraging LLMs such as Gemini and Mistral, multi-agent orchestration, and enhanced contextualization. The system achieves remarkable accurac…

Small Cues, Big Differences: Evaluating Interaction and Presentation for Annotation Retrieval in AR
Zahra Borhani, Ali Ebrahimpour-Boroojeny, Francisco R. Ortega
https://arxiv.org/abs/2509.11401

Small Cues, Big Differences: Evaluating Interaction and Presentation for Annotation Retrieval in AR
Augmented Reality (AR) enables intuitive interaction with virtual annotations overlaid on the real world, supporting a wide range of applications such as remote assistance, education, and industrial training. However, as the number of heterogeneous annotations increases, their efficient retrieval remains an open challenge in 3D environments. This paper examines how interaction modalities and presentation designs affect user performance, workload, fatigue, and preference in AR annotation retriev…

An alternative bootstrap procedure for factor-augmented regression models
Peiyun Jiang, Takashi Yamagata
https://arxiv.org/abs/2510.00947 https://arxiv.org…

An alternative bootstrap procedure for factor-augmented regression models
In this paper, we propose a novel bootstrap algorithm that is more efficient than existing methods for approximating the distribution of the factor-augmented regression estimator for a rotated parameter vector. The regression is augmented by $r$ factors extracted from a large panel of $N$ variables observed over $T$ time periods. We consider general weak factor (WF) models with $r$ signal eigenvalues that may diverge at different rates, $N^{α_{k}}$, where $0<α_{k}\leq 1$ for $k=1,2,...,r$. We…

IEEE Spectrum: Two visions for the future of AR smart glasses #AR/#AI

What Do Consumers Really Want in Smart Glasses?
As smart glasses become more mainstream, consumers choose between wearable tech that blends into daily life or stands out with immersive displays.

DyKnow-RAG: Dynamic Knowledge Utilization Reinforcement Framework for Noisy Retrieval-Augmented Generation in E-commerce Search Relevance
Tingqiao Xu, Shaowei Yao, Chenhe Dong, Yiming Jin, Zerui Huang, Dan Ou, Haihong Tang
https://arxiv.org/abs/2510.11122

DyKnow-RAG: Dynamic Knowledge Utilization Reinforcement Framework for Noisy Retrieval-Augmented Generation in E-commerce Search Relevance
Accurately modeling query-item relevance drives e-commerce ranking, yet long-tail, knowledge-heavy, and fast-evolving queries exceed parametric LLM coverage. External context (reviews, attribute encyclopedias, UGC) can help but is noisy, and single-pass latency and cost forbid any clean-then-summarize step. The model must, per query, judge relevance and decide whether to use, partially use, or ignore the context. DyKnow-RAG is a dynamic noisy-RAG framework built on Group Relative Policy Optimiz…

RAGs to Riches: RAG-like Few-shot Learning for Large Language Model Role-playing
Timothy Rupprecht, Enfu Nan, Arash Akbari, Arman Akbari, Lei Lu, Priyanka Maan, Sean Duffy, Pu Zhao, Yumei He, David Kaeli, Yanzhi Wang
https://arxiv.org/abs/2509.12168

RAGs to Riches: RAG-like Few-shot Learning for Large Language Model Role-playing
Role-playing Large language models (LLMs) are increasingly deployed in high-stakes domains such as healthcare, education, and governance, where failures can directly impact user trust and well-being. A cost effective paradigm for LLM role-playing is few-shot learning, but existing approaches often cause models to break character in unexpected and potentially harmful ways, especially when interacting with hostile users. Inspired by Retrieval-Augmented Generation (RAG), we reformulate LLM role-pl…

A Longitudinal Study on Different Annotator Feedback Loops in Complex RAG Tasks
Sara Rosenthal, Maeda Hanafi, Yannis Katsis, Lucian Popa, Marina Danilevsky
https://arxiv.org/abs/2510.11897

A Longitudinal Study on Different Annotator Feedback Loops in Complex RAG Tasks
Grounding conversations in existing passages, known as Retrieval-Augmented Generation (RAG), is an important aspect of Chat-Based Assistants powered by Large Language Models (LLMs) to ensure they are faithful and don't provide misinformation. Several benchmarks have been created to measure the performance of LLMs on this task. We present a longitudinal study comparing the feedback loop of an internal and external human annotator group for the complex annotation task of creating multi-turn RAG c…

See What I Mean? Mobile Eye-Perspective Rendering for Optical See-through Head-mounted Displays
Gerlinde Emsenhuber, Tobias Langlotz, Denis Kalkofen, Markus Tatzgern
https://arxiv.org/abs/2509.11653

See What I Mean? Mobile Eye-Perspective Rendering for Optical See-through Head-mounted Displays
Image-based scene understanding allows Augmented Reality systems to provide contextual visual guidance in unprepared, real-world environments. While effective on video see-through (VST) head-mounted displays (HMDs), such methods suffer on optical see-through (OST) HMDs due to misregistration between the world-facing camera and the user's eye perspective. To approximate the user's true eye view, we implement and evaluate three software-based eye-perspective rendering (EPR) techniques on a commer…

Crosslisted article(s) found for cs.IR. https://arxiv.org/list/cs.IR/new
[1/1]:
- Evaluating Retrieval-Augmented Generation Systems on Unanswerable, Uncheatable, Realistic, Multi-...
Gabrielle Kaili-May Liu, Bryan Li, Arman Cohan, William Gantt Walden, Eugene Yang

PrediQL: Automated Testing of GraphQL APIs with LLMs
Shaolun Liu, Sina Marefat, Omar Tsai, Yu Chen, Zecheng Deng, Jia Wang, Mohammad A. Tayebi
https://arxiv.org/abs/2510.10407 h…

PrediQL: Automated Testing of GraphQL APIs with LLMs
GraphQL's flexible query model and nested data dependencies expose APIs to complex, context-dependent vulnerabilities that are difficult to uncover using conventional testing tools. Existing fuzzers either rely on random payload generation or rigid mutation heuristics, failing to adapt to the dynamic structures of GraphQL schemas and responses. We present PrediQL, the first retrieval-augmented, LLM-guided fuzzer for GraphQL APIs. PrediQL combines large language model reasoning with adaptive fee…

Bidirectional Representations Augmented Autoregressive Biological Sequence Generation:Application in De Novo Peptide Sequencing
Xiang Zhang, Jiaqi Wei, Zijie Qiu, Sheng Xu, Zhi Jin, ZhiQiang Gao, Nanqing Dong, Siqi Sun
https://arxiv.org/abs/2510.08169

Bidirectional Representations Augmented Autoregressive Biological Sequence Generation:Application in De Novo Peptide Sequencing
Autoregressive (AR) models, common in sequence generation, are limited in many biological tasks such as de novo peptide sequencing and protein modeling by their unidirectional nature, failing to capture crucial global bidirectional token dependencies. Non-Autoregressive (NAR) models offer holistic, bidirectional representations but face challenges with generative coherence and scalability. To transcend this, we propose a hybrid framework enhancing AR generation by dynamically integrating rich c…

WARP -- Web-Augmented Real-time Program Repairer: A Real-Time Compilation Error Resolution using LLMs and Web-Augmented Synthesis
Anderson de Lima Luiz
https://arxiv.org/abs/2509.25192

WARP -- Web-Augmented Real-time Program Repairer: A Real-Time Compilation Error Resolution using LLMs and Web-Augmented Synthesis
Compilation errors represent a significant bottleneck in software development productivity. This paper introduces WARP (Web-Augmented Real-time Program Repairer), a novel system that leverages Large Language Models (LLMs) and dynamic web-augmented synthesis for real-time resolution of these errors. WARP actively monitors developer terminals, intelligently detects compilation errors, and synergistically combines the understanding of a fine-tuned Code-LLM with relevant solutions, explanations, an…

DSRAG: A Domain-Specific Retrieval Framework Based on Document-derived Multimodal Knowledge Graph
Mengzheng Yang, Yanfei Ren, David Osei Opoku, Ruochang Li, Peng Ren, Chunxiao Xing
https://arxiv.org/abs/2509.10467

DSRAG: A Domain-Specific Retrieval Framework Based on Document-derived Multimodal Knowledge Graph
Current general-purpose large language models (LLMs) commonly exhibit knowledge hallucination and insufficient domain-specific adaptability in domain-specific tasks, limiting their effectiveness in specialized question answering scenarios. Retrieval-augmented generation (RAG) effectively tackles these challenges by integrating external knowledge to enhance accuracy and relevance. However, traditional RAG still faces limitations in domain knowledge accuracy and context modeling.To enhance domain…

ARRC: Advanced Reasoning Robot Control - Knowledge-Driven Autonomous Manipulation Using Retrieval-Augmented Generation
Eugene Vorobiov, Ammar Jaleel Mahmood, Salim Rezvani, Robin Chhabra
https://arxiv.org/abs/2510.05547

ARRC: Advanced Reasoning Robot Control - Knowledge-Driven Autonomous Manipulation Using Retrieval-Augmented Generation
We present ARRC (Advanced Reasoning Robot Control), a practical system that connects natural-language instructions to safe local robotic control by combining Retrieval-Augmented Generation (RAG) with RGB-D perception and guarded execution on an affordable robot arm. The system indexes curated robot knowledge (movement patterns, task templates, and safety heuristics) in a vector database, retrieves task-relevant context for each instruction, and conditions a large language model (LLM) to produce…

Continuously Augmented Discrete Diffusion model for Categorical Generative Modeling
Huangjie Zheng, Shansan Gong, Ruixiang Zhang, Tianrong Chen, Jiatao Gu, Mingyuan Zhou, Navdeep Jaitly, Yizhe Zhang
https://arxiv.org/abs/2510.01329

Continuously Augmented Discrete Diffusion model for Categorical Generative Modeling
Standard discrete diffusion models treat all unobserved states identically by mapping them to an absorbing [MASK] token. This creates an 'information void' where semantic information that could be inferred from unmasked tokens is lost between denoising steps. We introduce Continuously Augmented Discrete Diffusion (CADD), a framework that augments the discrete state space with a paired diffusion in a continuous latent space. This yields graded, gradually corrupted states in which masked tokens a…

Knowledge-Decoupled Functionally Invariant Path with Synthetic Personal Data for Personalized ASR
Yue Gu, Zhihao Du, Ying Shi, Jiqing Han, Yongjun He
https://arxiv.org/abs/2510.10401

Knowledge-Decoupled Functionally Invariant Path with Synthetic Personal Data for Personalized ASR
Fine-tuning generic ASR models with large-scale synthetic personal data can enhance the personalization of ASR models, but it introduces challenges in adapting to synthetic personal data without forgetting real knowledge, and in adapting to personal data without forgetting generic knowledge. Considering that the functionally invariant path (FIP) framework enables model adaptation while preserving prior knowledge, in this letter, we introduce FIP into synthetic-data-augmented personalized ASR mo…

Differentially Private Synthetic Text Generation for Retrieval-Augmented Generation (RAG)
Junki Mori, Kazuya Kakizaki, Taiki Miyagawa, Jun Sakuma
https://arxiv.org/abs/2510.06719

Differentially Private Synthetic Text Generation for Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by grounding them in external knowledge. However, its application in sensitive domains is limited by privacy risks. Existing private RAG methods typically rely on query-time differential privacy (DP), which requires repeated noise injection and leads to accumulated privacy loss. To address this issue, we propose DP-SynRAG, a framework that uses LLMs to generate differentially private synthetic RAG databases. Unlike prior…

Replaced article(s) found for cs.SI. https://arxiv.org/list/cs.SI/new
[1/1]:
- Contrastive Learning Augmented Social Recommendations
Lin Wang, Weisong Wang, Xuanji Xiao, Qing Li

A Flexible Multi-Agent Deep Reinforcement Learning Framework for Dynamic Routing and Scheduling of Latency-Critical Services
Vincenzo Norman Vitale, Antonia Maria Tulino, Andreas F. Molisch, Jaime Llorca
https://arxiv.org/abs/2510.11535

A Flexible Multi-Agent Deep Reinforcement Learning Framework for Dynamic Routing and Scheduling of Latency-Critical Services
Timely delivery of delay-sensitive information over dynamic, heterogeneous networks is increasingly essential for a range of interactive applications, such as industrial automation, self-driving vehicles, and augmented reality. However, most existing network control solutions target only average delay performance, falling short of providing strict End-to-End (E2E) peak latency guarantees. This paper addresses the challenge of reliably delivering packets within application-imposed deadlines by l…

ExpertAgent: Enhancing Personalized Education through Dynamic Planning and Retrieval-Augmented Long-Chain Reasoning
Binrong Zhu, Guiran Liu, Nina Jiang
https://arxiv.org/abs/2510.07456

ExpertAgent: Enhancing Personalized Education through Dynamic Planning and Retrieval-Augmented Long-Chain Reasoning
The application of advanced generative artificial intelligence in education is often constrained by the lack of real-time adaptability, personalization, and reliability of the content. To address these challenges, we propose ExpertAgent - an intelligent agent framework designed for personalized education that provides reliable knowledge and enables highly adaptive learning experiences. Therefore, we developed ExpertAgent, an innovative learning agent that provides users with a proactive and per…

FSP-DETR: Few-Shot Prototypical Parasitic Ova Detection
Shubham Trehan, Udhav Ramachandran, Akash Rao, Ruth Scimeca, Sathyanarayanan N. Aakur
https://arxiv.org/abs/2510.09583 ht…

FSP-DETR: Few-Shot Prototypical Parasitic Ova Detection
Object detection in biomedical settings is fundamentally constrained by the scarcity of labeled data and the frequent emergence of novel or rare categories. We present FSP-DETR, a unified detection framework that enables robust few-shot detection, open-set recognition, and generalization to unseen biomedical tasks within a single model. Built upon a class-agnostic DETR backbone, our approach constructs class prototypes from original support images and learns an embedding space using augmented v…

Tool-Augmented Policy Optimization: Synergizing Reasoning and Adaptive Tool Use with Reinforcement Learning
Wenxun Wu, Yuanyang Li, Guhan Chen, Linyue Wang, Hongyang Chen
https://arxiv.org/abs/2510.07038

Tool-Augmented Policy Optimization: Synergizing Reasoning and Adaptive Tool Use with Reinforcement Learning
Recent advances in large language models (LLMs) have popularized test-time scaling, where models generate additional reasoning tokens before producing final answers. These approaches have demonstrated significant performance improvements on benchmarks involving mathematical reasoning. However, language models relying solely on direct inference still struggle with tasks demanding up-to-date knowledge or computational tools such as calculators and code interpreters for complex arithmetic operatio…

Exploiting Web Search Tools of AI Agents for Data Exfiltration
Dennis Rall, Bernhard Bauer, Mohit Mittal, Thomas Fraunholz
https://arxiv.org/abs/2510.09093 https://

Exploiting Web Search Tools of AI Agents for Data Exfiltration
Large language models (LLMs) are now routinely used to autonomously execute complex tasks, from natural language processing to dynamic workflows like web searches. The usage of tool-calling and Retrieval Augmented Generation (RAG) allows LLMs to process and retrieve sensitive corporate data, amplifying both their functionality and vulnerability to abuse. As LLMs increasingly interact with external data sources, indirect prompt injection emerges as a critical and evolving attack vector, enabling…

VersionRAG: Version-Aware Retrieval-Augmented Generation for Evolving Documents
Daniel Huwiler, Kurt Stockinger, Jonathan F\"urst
https://arxiv.org/abs/2510.08109 https://

VersionRAG: Version-Aware Retrieval-Augmented Generation for Evolving Documents
Retrieval-Augmented Generation (RAG) systems fail when documents evolve through versioning-a ubiquitous characteristic of technical documentation. Existing approaches achieve only 58-64% accuracy on version-sensitive questions, retrieving semantically similar content without temporal validity checks. We present VersionRAG, a version-aware RAG framework that explicitly models document evolution through a hierarchical graph structure capturing version sequences, content boundaries, and changes be…

TeamViewer-AR beschleunigt Prüfaufbauten bei Mercedes-AMG Petronas F1
Das Mercedes-AMG Petronas F1 Team nutzt die Augmented-Reality-Software Frontline von TeamViewer, um Prüfstände schnell und präzise zu montieren.

Retrieval-Augmented Code Generation: A Survey with Focus on Repository-Level Approaches
Yicheng Tao, Yao Qin, Yepang Liu
https://arxiv.org/abs/2510.04905 https://

Retrieval-Augmented Code Generation: A Survey with Focus on Repository-Level Approaches
Recent advancements in large language models (LLMs) have substantially improved automated code generation. While function-level and file-level generation have achieved promising results, real-world software development typically requires reasoning across entire repositories. This gives rise to the challenging task of Repository-Level Code Generation (RLCG), where models must capture long-range dependencies, ensure global semantic consistency, and generate coherent code spanning multiple files o…

PAD-TRO: Projection-Augmented Diffusion for Direct Trajectory Optimization
Jushan Chen, Santiago Paternain
https://arxiv.org/abs/2510.04436 https://arxiv.o…

PAD-TRO: Projection-Augmented Diffusion for Direct Trajectory Optimization
Recently, diffusion models have gained popularity and attention in trajectory optimization due to their capability of modeling multi-modal probability distributions. However, addressing nonlinear equality constraints, i.e, dynamic feasi- bility, remains a great challenge in diffusion-based trajectory optimization. Recent diffusion-based trajectory optimization frameworks rely on a single-shooting style approach where the denoised control sequence is applied to forward propagate the dynamical sy…

Beyond the Final Answer: Evaluating the Reasoning Trajectories of Tool-Augmented Agents
Wonjoong Kim, Sangwu Park, Yeonjun In, Sein Kim, Dongha Lee, Chanyoung Park
https://arxiv.org/abs/2510.02837

Beyond the Final Answer: Evaluating the Reasoning Trajectories of Tool-Augmented Agents
Although recent tool-augmented benchmarks incorporate complex user requests and diverse tools, the evaluation methods for most of them remain limited to answer matching. However, as the number of steps required to resolve a user request increases, a proper evaluation of an agent's performance must go beyond the final answer to also assess the problem-solving trajectory, including previously ignored aspects such as efficiency, hallucination, and adaptivity. The most straightforward method for ev…

Evaluating Hallucinations in Multimodal LLMs with Spoken Queries under Diverse Acoustic Conditions
Hansol Park, Hoseong Ahn, Junwon Moon, Yejin Lee, Kyuhong Shim
https://arxiv.org/abs/2510.08581

Evaluating Hallucinations in Multimodal LLMs with Spoken Queries under Diverse Acoustic Conditions
Hallucinations in vision-language models have been extensively studied using benchmarks that probe reliability in image-text settings. In contrast, the effect of spoken queries on multimodal hallucinations remains largely unexplored, despite the growing role of voice-driven interfaces. In this work, we investigate how spoken input influences hallucinations in multimodal large language models. We present RePOPE-Spk, an audio-augmented extension of the RePOPE benchmark, where queries are provided…

Grounding Large Language Models in Clinical Evidence: A Retrieval-Augmented Generation System for Querying UK NICE Clinical Guidelines
Matthew Lewis, Samuel Thio, Richard JB Dobson, Spiros Denaxas
https://arxiv.org/abs/2510.02967

Grounding Large Language Models in Clinical Evidence: A Retrieval-Augmented Generation System for Querying UK NICE Clinical Guidelines
This paper presents the development and evaluation of a Retrieval-Augmented Generation (RAG) system for querying the United Kingdom's National Institute for Health and Care Excellence (NICE) clinical guidelines using Large Language Models (LLMs). The extensive length and volume of these guidelines can impede their utilisation within a time-constrained healthcare system, a challenge this project addresses through the creation of a system capable of providing users with precisely matched informat…

VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering
Zhenghan Tai, Hanwei Wu, Qingchen Hu, Jijun Chi, Hailin He, Lei Ding, Tung Sum Thomas Kwok, Bohuai Xiao, Yuchen Hua, Suyuchen Wang, Peng Lu, Muzhi Li, Yihong Wu, Liheng Ma, Jerry Huang, Jiayi Zhang, Gonghao Zhang, Chaolong Jiang, Jingrui Tian, Sicheng Lyu, Zeyu Li, Boyu Han, Fengran Mo, Xinyue Yu, Yufei Cui, Ling Zhou, Xinyu Wang

VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering
Retrieval-Augmented Generation (RAG) is becoming increasingly essential for Question Answering (QA) in the financial sector, where accurate and contextually grounded insights from complex public disclosures are crucial. However, existing financial RAG systems face two significant challenges: (1) they struggle to process heterogeneous data formats, such as text, tables, and figures; and (2) they encounter difficulties in balancing general-domain applicability with company-specific adaptation. To…

Future G Network's New Reality: Opportunities and Security Challenges
Chandra Thapa, Surya Nepal
https://arxiv.org/abs/2510.09006 https://arxiv.org/pdf…

Future G Network's New Reality: Opportunities and Security Challenges
Future G network's new reality is a widespread cyber-physical environment created by Integrated Sensing and Communication (ISAC). It is a crucial technology that transforms wireless connections into ubiquitous sensors. ISAC unlocks transformative new capabilities, powering autonomous systems, augmented human sensing, and next-generation immersive applications, such as digital twins. However, this new reality fundamentally reshapes the security landscape. The primary security concern shifts from…

MotionRAG: Motion Retrieval-Augmented Image-to-Video Generation
Chenhui Zhu, Yilu Wu, Shuai Wang, Gangshan Wu, Limin Wang
https://arxiv.org/abs/2509.26391 https://

MotionRAG: Motion Retrieval-Augmented Image-to-Video Generation
Image-to-video generation has made remarkable progress with the advancements in diffusion models, yet generating videos with realistic motion remains highly challenging. This difficulty arises from the complexity of accurately modeling motion, which involves capturing physical constraints, object interactions, and domain-specific dynamics that are not easily generalized across diverse scenarios. To address this, we propose MotionRAG, a retrieval-augmented framework that enhances motion realism …

Ultraschall statt Kamera: LLMs erkennen Gesten zur Steuerung in Virtual Reality
Forscher wollen herkömmliches Tracking in Virtual Reality und Augmented Reality mit einer Kombination aus Schallwellenmessung und LLMs ersetzen.

Adaptive Tool Generation with Models as Tools and Reinforcement Learning
Chenpeng Wang, Xiaojie Cheng, Chunye Wang, Linfeng Yang, Lei Zhang
https://arxiv.org/abs/2510.06825 http…

Adaptive Tool Generation with Models as Tools and Reinforcement Learning
Tool-augmented language models have demonstrated strong capabilities, but their reliance on live API access creates scalability and reliability challenges during training and deployment. We propose MTR, a simulation-first training framework for tool-augmented reasoning. Instead of relying on live APIs, MTR learns from complete ReAct traces with schema-validated, simulated observations. Our approach operates through a multi-agent architecture where a ToolMaker generates task-specific, OpenAI-com…

FinVet: A Collaborative Framework of RAG and External Fact-Checking Agents for Financial Misinformation Detection
Daniel Berhane Araya, Duoduo Liao
https://arxiv.org/abs/2510.11654

FinVet: A Collaborative Framework of RAG and External Fact-Checking Agents for Financial Misinformation Detection
Financial markets face growing threats from misinformation that can trigger billions in losses in minutes. Most existing approaches lack transparency in their decision-making and provide limited attribution to credible sources. We introduce FinVet, a novel multi-agent framework that integrates two Retrieval-Augmented Generation (RAG) pipelines with external fact-checking through a confidence-weighted voting mechanism. FinVet employs adaptive three-tier processing that dynamically adjusts verifi…

Bloom: Designing for LLM-Augmented Behavior Change Interactions
Matthew J\"orke, Defne Gen\c{c}, Valentin Teutschbein, Shardul Sapkota, Sarah Chung, Paul Schmiedmayer, Maria Ines Campero, Abby C. King, Emma Brunskill, James A. Landay
https://arxiv.org/abs/2510.05449

Bloom: Designing for LLM-Augmented Behavior Change Interactions
Large language models (LLMs) offer novel opportunities to support health behavior change, yet existing work has narrowly focused on text-only interactions. Building on decades of HCI research demonstrating the effectiveness of UI-based interactions, we present Bloom, an application for physical activity promotion that integrates an LLM-based health coaching chatbot with established UI-based interactions. As part of Bloom's development, we conducted a redteaming evaluation and contribute a safet…

AgenticRAG: Tool-Augmented Foundation Models for Zero-Shot Explainable Recommender Systems
Bo Ma, Hang Li, ZeHua Hu, XiaoFan Gui, LuYao Liu, Simon Liu
https://arxiv.org/abs/2510.02668

AgenticRAG: Tool-Augmented Foundation Models for Zero-Shot Explainable Recommender Systems
Foundation models have revolutionized artificial intelligence, yet their application in recommender systems remains limited by reasoning opacity and knowledge constraints. This paper introduces AgenticRAG, a novel framework that combines tool-augmented foundation models with retrieval-augmented generation for zero-shot explainable recommendations. Our approach integrates external tool invocation, knowledge retrieval, and chain-of-thought reasoning to create autonomous recommendation agents capa…

AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications
Linh The Nguyen, Chi Tran, Dung Ngoc Nguyen, Van-Cuong Pham, Hoang Ngo, Dat Quoc Nguyen
https://arxiv.org/abs/2510.02243

AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications
We introduce AccurateRAG -- a novel framework for constructing high-performance question-answering applications based on retrieval-augmented generation (RAG). Our framework offers a pipeline for development efficiency with tools for raw dataset processing, fine-tuning data generation, text embedding & LLM fine-tuning, output evaluation, and building RAG systems locally. Experimental results show that our framework outperforms previous strong baselines and obtains new state-of-the-art question-a…

Memory-Augmented Log Analysis with Phi-4-mini: Enhancing Threat Detection in Structured Security Logs
Anbi Guo, Mahfuza Farooque
https://arxiv.org/abs/2510.00529 https://…

Memory-Augmented Log Analysis with Phi-4-mini: Enhancing Threat Detection in Structured Security Logs
Structured security logs are critical for detecting advanced persistent threats (APTs). Large language models (LLMs) struggle in this domain due to limited context and domain mismatch. We propose \textbf{DM-RAG}, a dual-memory retrieval-augmented generation framework for structured log analysis. It integrates a short-term memory buffer for recent summaries and a long-term FAISS-indexed memory for historical patterns. An instruction-tuned Phi-4-mini processes the combined context and outputs str…

Benchmarking Foundation Models with Retrieval-Augmented Generation in Olympic-Level Physics Problem Solving
Shunfeng Zheng, Yudi Zhang, Meng Fang, Zihan Zhang, Zhitan Wu, Mykola Pechenizkiy, Ling Chen
https://arxiv.org/abs/2510.00919

Benchmarking Foundation Models with Retrieval-Augmented Generation in Olympic-Level Physics Problem Solving
Retrieval-augmented generation (RAG) with foundation models has achieved strong performance across diverse tasks, but their capacity for expert-level reasoning-such as solving Olympiad-level physics problems-remains largely unexplored. Inspired by the way students prepare for competitions by reviewing past problems, we investigate the potential of RAG to enhance physics reasoning in foundation models. We introduce PhoPile, a high-quality multimodal dataset specifically designed for Olympiad-lev…

Investigating Encoding and Perspective for Augmented Reality
Jade Kandel, Sriya Kasumarthi, Spiros Tsalikis, Chelsea Duppen, Daniel Szafir, Michael Lewek, Henry Fuchs, Danielle Szafir
https://arxiv.org/abs/2510.00407

Investigating Encoding and Perspective for Augmented Reality
Augmented reality (AR) offers promising opportunities to support movement-based activities, such as personal training or physical therapy, with real-time, spatially-situated visual cues. While many approaches leverage AR to guide motion, existing design guidelines focus on simple, upper-body movements within the user's field of view. We lack evidence-based design recommendations for guiding more diverse scenarios involving movements with varying levels of visibility and direction. We conducted …

External Data Extraction Attacks against Retrieval-Augmented Large Language Models
Yu He, Yifei Chen, Yiming Li, Shuo Shao, Leyi Qi, Boheng Li, Dacheng Tao, Zhan Qin
https://arxiv.org/abs/2510.02964

External Data Extraction Attacks against Retrieval-Augmented Large Language Models
In recent years, RAG has emerged as a key paradigm for enhancing large language models (LLMs). By integrating externally retrieved information, RAG alleviates issues like outdated knowledge and, crucially, insufficient domain expertise. While effective, RAG introduces new risks of external data extraction attacks (EDEAs), where sensitive or copyrighted data in its knowledge base may be extracted verbatim. These risks are particularly acute when RAG is used to customize specialized LLM applicati…

Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrieval
Yingyi Zhang, Pengyue Jia, Derong Xu, Yi Wen, Xianneng Li, Yichao Wang, Wenlin Zhang, Xiaopeng Li, Weinan Gan, Huifeng Guo, Yong Liu, Xiangyu Zhao
https://arxiv.org/abs/2510.08935

Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrieval
Retrieval-Augmented Generation (RAG) critically depends on effective query expansion to retrieve relevant information. However, existing expansion methods adopt uniform strategies that overlook user-specific semantics, ignoring individual expression styles, preferences, and historical context. In practice, identical queries in text can express vastly different intentions across users. This representational rigidity limits the ability of current RAG systems to generalize effectively in personali…

Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs
Ziliang Wang, Kang An, Xuhui Zheng, Faqiang Qian, Weikun Zhang, Cijun Ouyang, Jialu Cai, Yuhang Wang, Yichao Wu
https://arxiv.org/abs/2510.00861

Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs
While search-augmented large language models (LLMs) exhibit impressive capabilities, their reliability in complex multi-hop reasoning remains limited. This limitation arises from three fundamental challenges: decomposition errors, where tasks are incorrectly broken down; retrieval missing, where key evidence fails to be retrieved; and reasoning errors, where flawed logic propagates through the reasoning chain. A single failure in any of these stages can derail the final answer. We propose Erasa…

HalluGuard: Evidence-Grounded Small Reasoning Models to Mitigate Hallucinations in Retrieval-Augmented Generation
Loris Bergeron, Ioana Buhnila, J\'er\^ome Fran\c{c}ois, Radu State
https://arxiv.org/abs/2510.00880

HalluGuard: Evidence-Grounded Small Reasoning Models to Mitigate Hallucinations in Retrieval-Augmented Generation
Large Language Models (LLMs) excel in many NLP tasks but remain prone to hallucinations, limiting trust in real-world applications. We present HalluGuard, a 4B-parameter Small Reasoning Model (SRM) for mitigating hallucinations in Retrieval-Augmented Generation (RAG). HalluGuard classifies document-claim pairs as grounded or hallucinated and produces evidence-grounded justifications for transparency. Our approach combines (i) a domain-agnostic synthetic dataset derived from FineWeb and refined …

Generative Data Augmentation in Graph Contrastive Learning for Recommendation
Yansong Wang, Qihui Lin, Junjie Huang, Tao Jia
https://arxiv.org/abs/2510.09129 https://

Generative Data Augmentation in Graph Contrastive Learning for Recommendation
Recommendation systems have become indispensable in various online platforms, from e-commerce to streaming services. A fundamental challenge in this domain is learning effective embeddings from sparse user-item interactions. While contrastive learning has recently emerged as a promising solution to this issue, generating augmented views for contrastive learning through most existing random data augmentation methods often leads to the alteration of original semantic information. In this paper, w…