Tootfinder

Multi-Agent Debate Strategies to Enhance Requirements Engineering with Large Language Models
Marc Oriol, Quim Motger, Jordi Marco, Xavier Franch
https://arxiv.org/abs/2507.05981

Multi-Agent Debate Strategies to Enhance Requirements Engineering with Large Language Models
Context: Large Language Model (LLM) agents are becoming widely used for various Requirements Engineering (RE) tasks. Research on improving their accuracy mainly focuses on prompt engineering, model fine-tuning, and retrieval augmented generation. However, these methods often treat models as isolated black boxes - relying on single-pass outputs without iterative refinement or collaboration, limiting robustness and adaptability. Objective: We propose that, just as human debates enhance accuracy a…

"We need to avail ourselves of GenAI to enhance knowledge distribution": Empowering Older Adults through GenAI Literacy
Eunhye Grace Ko, Shaini Nanayakkara, Earl W. Huff Jr
https://arxiv.org/abs/2506.06225

"We need to avail ourselves of GenAI to enhance knowledge distribution": Empowering Older Adults through GenAI Literacy
As generative AI (GenAI) becomes increasingly widespread, it is crucial to equip users, particularly vulnerable populations such as older adults (65 and older), with the knowledge to understand its benefits and potential risks. Older adults often exhibit greater reservations about adopting emerging technologies and require tailored literacy support. Using a mixed methods approach, this study examines strategies for delivering GenAI literacy to older adults through a chatbot named Litti, evaluat…

"The real threat posed by generative AI is not that it will eliminate work on a mass scale, rendering human labour obsolete. It is that, left unchecked, it will continue to transform work in ways that deepen precarity, intensify surveillance, and widen existing inequalities."
"The current trajectory of generative AI reflects the priorities of firms seeking to lower costs, discipline workers, and consolidate profits — not any drive to enhance human flourishing. If we allo…

Is the AI Bubble About to Burst?
In the years since Automation and the Future of Work first appeared, a new wave of technological enthusiasm has swept across the popular imagination. The catalyst this time has been the rapid advances in generative artificial intelligence, spearheaded by companies like OpenAI, Google DeepMind, and Meta. Once again, a c

Today seems like a good day to repost this piece I wrote a few weeks back.
How CISOs can defend against Scattered Spider ransomware attacks
https://www.csoonline.com/article/3994369/how-cisos-can-defend-against-scattered-s…

How CISOs can defend against Scattered Spider ransomware attacks
CISOs should fortify help desk and employee defenses, enhance intrusion detection and tracking capabilities, and recognize that paying ransoms is not a viable strategy.

Apple adds a 3x playback option for podcasts in iOS26, an Enhance Dialogue tool to hear speech more clearly, and a puzzle game for Apple News Plus subscribers (Jay Peters/The Verge)
https://www.theverge.com/news/684708/apple-podcasts-speed-news-emoji-game…

Apple turns up the speed on Podcasts and adds a new emoji game to News
Apple will let users listen to podcasts at up to 3x speed in the Podcast app, and Apple News Plus subscribers will be able to play a new Emoji Game.

Can Contextualized Physics Problems Enhance Student Motivation? Uncovering a Student-Teacher Perception Gap through a Large-Scale Survey
Yajun Wei, Xinting Peng, Yi Zhong, Feipeng Pi, Yanfang Zhai, Lei Bao
https://arxiv.org/abs/2507.05586

Can Contextualized Physics Problems Enhance Student Motivation? Uncovering a Student-Teacher Perception Gap through a Large-Scale Survey
Embedding physics problems unreal-world settings, here termed contextualized physics problems (CPP), is widely believed to foster students' interest, motivation, and learning. However, firm evidence for this claim remains scarce. To explore this issue, we surveyed 868 secondary students and 154 teachers to examine their attitudes toward CPP and investigate whether students and teachers perceive these problems as promoting student interest and motivation in learning physics. The findings reveal …

Alcohol additives to enhance ammonia-methane combustion efficiency and reduce emissions: A reactive force field analysis
Amirali Shateri, Zhiyin Yang, Jianfei Xie, Nasser Sherkat
https://arxiv.org/abs/2507.06314

Alcohol additives to enhance ammonia-methane combustion efficiency and reduce emissions: A reactive force field analysis
Exploring the impact of alcohol additives on combustion and pyrolysis of ammonia/methane is of great importance in the pursuit of sustainable energy technologies. This work employs Reactive Force Field (ReaxFF) molecular dynamics (MD) simulations to investigate the underlying mechanism of how ethanol and methanol additives affect reaction pathways, NOx emissions and bond energy characteristics in ammonia-methane pyrolysis and combustion processes. It shows that adding alcohols altered NOx forma…

This https://arxiv.org/abs/2506.01348 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Distributionally Robust Learning in Survival Analysis
We introduce an innovative approach that incorporates a Distributionally Robust Learning (DRL) approach into Cox regression to enhance the robustness and accuracy of survival predictions. By formulating a DRL framework with a Wasserstein distance-based ambiguity set, we develop a variant Cox model that is less sensitive to assumptions about the underlying data distribution and more resilient to model misspecification and data perturbations. By leveraging Wasserstein duality, we reformulate the …

This https://arxiv.org/abs/2506.03978 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Structured Pruning for Diverse Best-of-N Reasoning Optimization
Model pruning in transformer-based language models, traditionally viewed as a means of achieving computational savings, can enhance the model's reasoning capabilities. In this work, we uncover a surprising phenomenon: the selective pruning of certain attention heads leads to improvements in reasoning performance, particularly on challenging tasks. Motivated by this observation, we propose SPRINT, a novel contrastive learning framework that dynamically selects the optimal head and layer to prune…

AR2: Attention-Guided Repair for the Robustness of CNNs Against Common Corruptions
Fuyuan Zhang, Qichen Wang, Jianjun Zhao
https://arxiv.org/abs/2507.06332

AR2: Attention-Guided Repair for the Robustness of CNNs Against Common Corruptions
Deep neural networks suffer from significant performance degradation when exposed to common corruptions such as noise, blur, weather, and digital distortions, limiting their reliability in real-world applications. In this paper, we propose AR2 (Attention-Guided Repair for Robustness), a simple yet effective method to enhance the corruption robustness of pretrained CNNs. AR2 operates by explicitly aligning the class activation maps (CAMs) between clean and corrupted images, encouraging the model…

Enhancing Measurement Precision of Non-Degenerate Two-Photon Absorption
Gaurav Shukla, Shahram Panahiyan, Devendra Kumar Mishra, Frank Schlawin
https://arxiv.org/abs/2506.07384

Enhancing Measurement Precision of Non-Degenerate Two-Photon Absorption
Recent theoretical and experimental studies have shown that squeezed states of light can be engineered to enhance the resolution of nonlinear optical measurements. Here, we analyze non-degenerate two-photon absorption signals obtained from transmission measurements using two-mode squeezed light and compare different measurement strategies. In particular, we investigate how correlations between the light modes may be used to improve the achievable precision. We find that intensity correlation me…

Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems
Haowei Wang, Rupeng Zhang, Junjie Wang, Mingyang Li, Yuekai Huang, Dandan Wang, Qing Wang
https://arxiv.org/abs/2506.06151

Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems
Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by retrieving relevant documents from external corpora before generating responses. This approach significantly expands LLM capabilities by leveraging vast, up-to-date external knowledge. However, this reliance on external knowledge makes RAG systems vulnerable to corpus poisoning attacks that manipulate generated outputs via poisoned document injection. Existing poisoning attack strategies typically treat the ret…

On-board Mission Replanning for Adaptive Cooperative Multi-Robot Systems
Elim Kwan, Rehman Qureshi, Liam Fletcher, Colin Laganier, Victoria Nockles, Richard Walters
https://arxiv.org/abs/2506.06094

On-board Mission Replanning for Adaptive Cooperative Multi-Robot Systems
Cooperative autonomous robotic systems have significant potential for executing complex multi-task missions across space, air, ground, and maritime domains. But they commonly operate in remote, dynamic and hazardous environments, requiring rapid in-mission adaptation without reliance on fragile or slow communication links to centralised compute. Fast, on-board replanning algorithms are therefore needed to enhance resilience. Reinforcement Learning shows strong promise for efficiently solving mi…

This https://arxiv.org/abs/2506.05069 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Reason-to-Recommend: Using Interaction-of-Thought Reasoning to Enhance LLM Recommendation
Driven by advances in Large Language Models (LLMs), integrating them into recommendation tasks has gained interest due to their strong semantic understanding and prompt flexibility. Prior work encoded user-item interactions or metadata into prompts for recommendations. In parallel, LLM reasoning, boosted by test-time scaling and reinforcement learning, has excelled in fields like mathematics and code, where reasoning traces and correctness signals are clear, enabling high performance and interp…

ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease
Zhiyuan Yang, Kai Li, Sophia Ghamoshi Ramandi, Patricia Brassard, Hakim Khellaf, Vincent Quoc-Huy Trinh, Jennifer Zhang, Lina Chen, Corwyn Rowsell, Sonal Varma, Kostas Plataniotis, Mahdi S. Hosseini
https://ar…

ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease
Computational pathology (CoPath) leverages histopathology images to enhance diagnostic precision and reproducibility in clinical pathology. However, publicly available datasets for CoPath that are annotated with extensive histological tissue type (HTT) taxonomies at a granular level remain scarce due to the significant expertise and high annotation costs required. Existing datasets, such as the Atlas of Digital Pathology (ADP), address this by offering diverse HTT annotations generalized to mul…

It would enhance Alan Dershowitz's reputation if he voluntarily admitted to statutory rape.

Too Human to Model:The Uncanny Valley of LLMs in Social Simulation -- When Generative Language Agents Misalign with Modelling Principles
Yongchao Zeng, Calum Brown, Mark Rounsevell
https://arxiv.org/abs/2507.06310

Too Human to Model:The Uncanny Valley of LLMs in Social Simulation -- When Generative Language Agents Misalign with Modelling Principles
Large language models (LLMs) have been increasingly used to build agents in social simulation because of their impressive abilities to generate fluent, contextually coherent dialogues. Such abilities can enhance the realism of models. However, the pursuit of realism is not necessarily compatible with the epistemic foundation of modelling. We argue that LLM agents, in many regards, are too human to model: they are too expressive, detailed and intractable to be consistent with the abstraction, si…

Capacity of MIMO Systems Aided by Microwave Linear Analog Computers (MiLACs)
Matteo Nerini, Bruno Clerckx
https://arxiv.org/abs/2506.05983 https://

Capacity of MIMO Systems Aided by Microwave Linear Analog Computers (MiLACs)
Future wireless systems, known as gigantic multiple-input multiple-output (MIMO), are expected to enhance performance by significantly increasing the number of antennas, e.g., a few thousands. To enable gigantic MIMO overcoming the scalability limitations of digital architectures, microwave linear analog computers (MiLACs) have recently emerged. A MiLAC is a multiport microwave network that processes input microwave signals entirely in the analog domain, thereby reducing hardware costs and comp…

AI-based Environment-Aware XL-MIMO Channel Estimation with Location-Specific Prior Knowledge Enabled by CKM
Yuelong Qiu, Di Wu, Yong Zeng, Yanqun Tang, Nan Cheng, Chenhao Qi
https://arxiv.org/abs/2507.06066

AI-based Environment-Aware XL-MIMO Channel Estimation with Location-Specific Prior Knowledge Enabled by CKM
Accurate and efficient acquisition of wireless channel state information (CSI) is crucial to enhance the communication performance of wireless systems. However, with the continuous densification of wireless links, increased channel dimensions, and the use of higher-frequency bands, channel estimation in the sixth generation (6G) and beyond wireless networks faces new challenges, such as insufficient orthogonal pilot sequences, inadequate signal-to-noise ratio (SNR) for channel training, and mor…

VaxPulse: Monitoring of Online Public Concerns to Enhance Post-licensure Vaccine Surveillance
Muhammad Javed, Sedigh Khademi, Joanne Hickman, Jim Buttery, Hazel Clothier, Gerardo Luis Dimaguila
https://arxiv.org/abs/2507.04656

VaxPulse: Monitoring of Online Public Concerns to Enhance Post-licensure Vaccine Surveillance
The recent vaccine-related infodemic has amplified public concerns, highlighting the need for proactive misinformation management. We describe how we enhanced the reporting surveillance system of Victoria's vaccine safety service, SAEFVIC, through the incorporation of new information sources for public sentiment analysis, topics of discussion, and hesitancies about vaccinations online. Using VaxPulse, a multi-step framework, we integrate adverse events following immunisation (AEFI) with sentime…

Forex Trading Robot Using Fuzzy Logic
Mustafa Shabani, Alireza Nasiri, Hassan Nafardi
https://arxiv.org/abs/2507.06383 https://arxiv.…

Forex Trading Robot Using Fuzzy Logic
In this study, we propose a fuzzy system for conducting short-term transactions in the forex market. The system is designed to enhance common strategies in the forex market using fuzzy logic, thereby improving the accuracy of transactions. Traditionally, technical strategies based on oscillator indicators have relied on predefined ranges for indicators such as Relative Strength Index (RSI), Commodity Channel Indicator (CCI), and Stochastic to determine entry points for trades. However, the use …

Structure-Informed Learning of Flat Band 2D Materials
Xiangwen Wang, Yihao Wei, Anupam Bhattacharya, Qian Yang, Artem Mishchenko
https://arxiv.org/abs/2506.07518

Structure-Informed Learning of Flat Band 2D Materials
Flat electronic bands enhance electron-electron interactions and give rise to correlated states such as unconventional superconductivity or fractional topological phases. However, most current efforts towards flat-band materials discovery rely on density functional theory (DFT) calculations and manual band structures inspection, restraining their applicability to vast unexplored material spaces. While data-driven methods offer a scalable alternative, most existing models either depend on band s…

This https://arxiv.org/abs/2410.02676 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Matter power spectrum induced by primordial magnetic fields: from the linear to the non-linear regime
Linear theory predicts that primordial magnetic fields (PMFs) enhance the matter power spectrum on small scales. However, the linear approximation breaks down on sufficiently small scales where PMF-induced baryon perturbations back-react onto the magnetic fields. Previous studies assumed that the baryon power spectrum would be sharply suppressed in this non-linear regime, based on arguments related to the magnetic Jeans scale. For the first time, we perform dedicated magnetohydrodynamic (MHD) s…

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine
Anushka Jha, Tanushree Dewangan, Mukul Lokhande, Santosh Kumar Vishvakarma
https://arxiv.org/abs/2506.07046

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine
Reinforcement Learning (RL) has outperformed other counterparts in sequential decision-making and dynamic environment control. However, FPGA deployment is significantly resource-expensive, as associated with large number of computations in training agents with high-quality images and possess new challenges. In this work, we propose QForce-RL takes benefits of quantization to enhance throughput and reduce energy footprint with light-weight RL architecture, without significant performance degrada…

Really developing a loathing of corporate no-reply email addresses. Whatever else they do they don't enhance customer service.

This https://arxiv.org/abs/2503.12177 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Open Wireless Digital Twin: End-to-End 5G Mobility Emulation in O-RAN Framework
This paper presents an end-to-end wireless digital twin platform constructed using open-source software and open data to enhance the evaluation of mobile communication systems. The proposed open wireless digital twin (OWDT) integrates OpenAirInterface (OAI) for 5G NR protocol stack emulation and NVIDIA Sionna RT for high-resolution ray tracing based radio propagation modeling. This integration enables realistic emulation of vehicular mobility scenarios, leveraging real-world geographic and buil…

Fusion of heterogeneous data for robust degradation prognostics
Edgar Jaber (EDF R\&D PRISME, CB, DATAFLOT), Emmanuel Remy (EDF R\&D PRISME), Vincent Chabridon (EDF R\&D PRISME), Mathilde Mougeot (ENSIIE, CB), Didier Lucor (DATAFLOT)
https://arxiv.org/abs/2506.05882

Fusion of heterogeneous data for robust degradation prognostics
Assessing the degradation state of an industrial asset first requires evaluating its current condition and then to project the forecast model trajectory to a predefined prognostic threshold, thereby estimating its remaining useful life (RUL). Depending on the available information, two primary categories of forecasting models may be used: physics-based simulation codes and datadriven (machine learning) approaches. Combining both modelling approaches may enhance prediction robustness, especially…

The scaling regimes for unsteady diffusion across particle-stabilized fluid interfaces
T. J. J. M. van Overveld, V. Garbin
https://arxiv.org/abs/2506.05366

The scaling regimes for unsteady diffusion across particle-stabilized fluid interfaces
Colloidal particles at fluid interfaces can enhance the stability of drops and bubbles. Yet, their effect on mass transfer in these multiphase systems remains ambiguous, with some experiments reporting strongly hindered diffusion, while others show nearly no effect, even at near-complete surface coverage. To resolve this ambiguity, we solve the Fick-Jacobs equation for unsteady diffusion, allowing us to treat the particle-laden interface as a locally reduced cross-sectional area for mass transf…

This https://arxiv.org/abs/2506.01644 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A Budgeted Multi-Level Monte Carlo Method for Full Field Estimates of Multi-PDE Problems
We present a high-performance budgeted multi-level Monte Carlo method for estimates on the entire spatial domain of multi-PDE problems with random input data. The method is designed to operate optimally within memory and CPU-time constraints and eliminates the need for a priori knowledge of the problem's regularity and the algorithm's potential memory demand. To achieve this, we build on the budgeted multi-level Monte Carlo framework and enhance it with a sparse multi-index update algorithm ope…

Dense Associative Memory in a Nonlinear Optical Hopfield Neural Network
Khalid Musa, Santosh Kumar, Michael Katidis, Yu-Ping Huang
https://arxiv.org/abs/2506.07849

Dense Associative Memory in a Nonlinear Optical Hopfield Neural Network
Modern Hopfield Neural Networks (HNNs), also known as Dense Associative Memories (DAMs), enhance the performance of simple recurrent neural networks by leveraging the nonlinearities in their energy functions. They have broad applications in combinatorial optimization, high-capacity memory storage, deep learning transformers, and correlated pattern recognition. Thus far, research on DAMs has been primarily theoretical, with implementations limited to CPUs and GPUs. In this work, for the first ti…

SVD: Spatial Video Dataset
M. H. Izadimehr, Milad Ghanbari, Guodong Chen, Wei Zhou, Xiaoshuai Hao, Mallesham Dasari, Christian Timmerer, Hadi Amirpour
https://arxiv.org/abs/2506.06037

SVD: Spatial Video Dataset
Stereoscopic video has long been the subject of research due to its capacity to deliver immersive three-dimensional content across a wide range of applications, from virtual and augmented reality to advanced human-computer interaction. The dual-view format inherently provides binocular disparity cues that enhance depth perception and realism, making it indispensable for fields such as telepresence, 3D mapping, and robotic vision. Until recently, however, end-to-end pipelines for capturing, enco…

This https://arxiv.org/abs/2506.02052 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Protap: A Benchmark for Protein Modeling on Realistic Downstream Applications
Recently, extensive deep learning architectures and pretraining strategies have been explored to support downstream protein applications. Additionally, domain-specific models incorporating biological knowledge have been developed to enhance performance in specialized tasks. In this work, we introduce $\textbf{Protap}$, a comprehensive benchmark that systematically compares backbone architectures, pretraining strategies, and domain-specific models across diverse and realistic downstream protein …

Replaced article(s) found for cs.MA. https://arxiv.org/list/cs.MA/new
[1/1]:
- An AI Theory of Mind Will Enhance Our Collective Intelligence
Michael S. Harr\'e, Catherine Drysdale, Jaime Ruiz-Serra

Heterogeneous Causal Learning for Optimizing Aggregated Functions in User Growth
Shuyang Du, Jennifer Zhang, Will Y. Zou
https://arxiv.org/abs/2507.05510 h…

Heterogeneous Causal Learning for Optimizing Aggregated Functions in User Growth
User growth is a major strategy for consumer internet companies. To optimize costly marketing campaigns and maximize user engagement, we propose a novel treatment effect optimization methodology to enhance user growth marketing. By leveraging deep learning, our algorithm learns from past experiments to optimize user selection and reward allocation, maximizing campaign impact while minimizing costs. Unlike traditional prediction methods, our model directly models uplifts in key business metrics.…

GloBIAS: strengthening the foundations of BioImage Analysis
A. A. Corbat (BioImage Informatics Unit, Science for Life Laboratory and Department of Information Technology, Uppsala University, Sweden), C. G. Walther (German BioImaging, Gesellschaft f\"ur Mikroskopie und Bildanalyse e.V., Konstanz, Germany, University of Vienna, Vienna, Austria), L. R. de la Ballina (Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Montebe…

GloBIAS: strengthening the foundations of BioImage Analysis
There is a global need for BioImage Analysis (BIA) as advances in life sciences increasingly rely on cutting-edge imaging systems that have dramatically expanded the complexity and dimensionality of biological images. Turning these data into scientific discoveries requires people with effective data management skills and knowledge of state-of-the-art image processing and data analysis, in other words, BioImage Analysts. The Global BioImage Analysts' Society (GloBIAS) aims to enhance the profile…

This https://arxiv.org/abs/2501.05526 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Introducing new resonant soft x-ray scattering capability in SSRL
Resonant soft X-ray scattering (RSXS) is a powerful technique for probing both spatial and electronic structures within solid-state systems. We present a newly developed RSXS capability at beamline 13-3 of the Stanford Synchrotron Radiation Lightsource (SSRL), designed to enhance materials science research. This advanced setup achieves a base sample temperature as low as 9.8 K combined with extensive angular motions (azimuthal ϕand flipping χ), enabling comprehensive exploration of reciprocal…

Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection
Yupeng Hu, Changxing Ding, Chang Sun, Shaoli Huang, Xiangmin Xu
https://arxiv.org/abs/2507.06510

Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection
Open vocabulary Human-Object Interaction (HOI) detection is a challenging task that detects all triplets of interest in an image, even those that are not pre-defined in the training set. Existing approaches typically rely on output features generated by large Vision-Language Models (VLMs) to enhance the generalization ability of interaction representations. However, the visual features produced by VLMs are holistic and coarse-grained, which contradicts the nature of detect…

Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100
Sukru Selim Calik, Andac Akyuz, Zeynep Hilal Kilimci, Kerem Colak
https://arxiv.org/abs/2506.06345

Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100
Financial literacy is increasingly dependent on the ability to interpret complex financial data and utilize advanced forecasting tools. In this context, this study proposes a novel approach that combines transformer-based time series models with explainable artificial intelligence (XAI) to enhance the interpretability and accuracy of stock price predictions. The analysis focuses on the daily stock prices of the five highest-volume banks listed in the BIST100 index, along with XBANK and XU100 in…

Enhanced Andreev Reflection in Flat-Band Systems: Wave Packet Dynamics, DC Transport and the Josephson Effect
Sarbajit Mazumdar, Anamitra Mukherjee, Kush Saha, Sourin Das
https://arxiv.org/abs/2507.06327

Enhanced Andreev Reflection in Flat-Band Systems: Wave Packet Dynamics, DC Transport and the Josephson Effect
We investigate Andreev reflection (AR) in a proximity-induced normal-superconductor (NS) junction within the extended $α-\mathcal{T}_3$ lattice, emphasizing the impact of flat bands on AR. Our findings reveal that flat bands significantly enhance AR. Through wave packet dynamics, we track the real-time evolution of quasi-particle wave packets across the junction, providing deeper insight into electron-hole conversion. Notably, the combination of band flatness and anisotropic dispersion in the …

High Impedance Granular Aluminum Ring Resonators
Mahya Khorramshahi, Martin Spiecker, Patrick Paluch, Simon Geisert, Nicolas Gosling, Nicolas Zapata, Lucas Brauch, Christian Kuebel, Simone Dehm, Ralph Krupke, Wolfgang Wernsdorfer, Ioan M. Pop, Thomas Reisinger
https://arxiv.org/abs/2506.07761

High Impedance Granular Aluminum Ring Resonators
Superconducting inductors with impedance surpassing the resistance quantum, i.e., superinductors, are important for quantum technologies because they enable the development of protected qubits, enhance coupling to systems with small electric dipole moments, and facilitate the study of phase-slip physics. We demonstrate superinductors with densely packed meandered traces of granular aluminum (grAl) with inductances up to $4\,μ\mathrm{H}$, achieving impedances exceeding $100\,\mathrm{k}Ω$ in th…

Survey of LLM Agent Communication with MCP: A Software Design Pattern Centric Review
Anjana Sarkar, Soumyendu Sarkar
https://arxiv.org/abs/2506.05364 https…

Survey of LLM Agent Communication with MCP: A Software Design Pattern Centric Review
This survey investigates how classical software design patterns can enhance the reliability and scalability of communication in Large Language Model (LLM)-driven agentic AI systems, focusing particularly on the Model Context Protocol (MCP). It examines the foundational architectures of LLM-based agents and their evolution from isolated operation to sophisticated, multi-agent collaboration, addressing key communication hurdles that arise in this transition. The study revisits well-established pa…

Uncertainty-Aware Strategies: A Model-Agnostic Framework for Robust Financial Optimization through Subsampling
Hans Buehler, Blanka Horvath, Yannick Limmer, Thorsten Schmidt
https://arxiv.org/abs/2506.07299

Uncertainty-Aware Strategies: A Model-Agnostic Framework for Robust Financial Optimization through Subsampling
This paper addresses the challenge of model uncertainty in quantitative finance, where decisions in portfolio allocation, derivative pricing, and risk management rely on estimating stochastic models from limited data. In practice, the unavailability of the true probability measure forces reliance on an empirical approximation, and even small misestimations can lead to significant deviations in decision quality. Building on the framework of Klibanoff et al. (2005), we enhance the conventional ob…

Conversational Interfaces for Parametric Conceptual Architectural Design: Integrating Mixed Reality with LLM-driven Interaction
Ruochen Ji, Lyu Tiangang
https://arxiv.org/abs/2506.06066

Conversational Interfaces for Parametric Conceptual Architectural Design: Integrating Mixed Reality with LLM-driven Interaction
Mixed reality (MR) environments offer embodied spatial interaction, providing intuitive 3D manipulation capabilities that enhance the conceptual design process. Parametric modeling, a powerful and advanced architectural design method, enables the generation of complex, optimized geometries. However, its integration into MR environments remains limited due to precision constraints and unsuitable input modalities. Existing MR tools prioritize spatial interaction but lack the control and expressiv…

A Survey on Artificial Noise for Physical Layer Security: Opportunities, Technologies, Guidelines, Advances, and Trends
Hong Niu, Yue Xiao, Xia Lei, Jiangong Chen, Zhihan Xiao, Mao Li, Chau Yuen
https://arxiv.org/abs/2507.06500

A Survey on Artificial Noise for Physical Layer Security: Opportunities, Technologies, Guidelines, Advances, and Trends
Due to the broadcast nature of wireless communications, physical-layer security has attracted increasing concerns from both academia and industry. Artificial noise (AN), as one of the promising physical-layer security techniques, is capable of utilizing the spatial degree-of-freedom of channels to effectively enhance the security of wireless communications. In contrast to other physicallayer security techniques, the key distinguishing feature of AN is to generate specific interfering signals ac…

PAPRLE (Plug-And-Play Robotic Limb Environment): A Modular Ecosystem for Robotic Limbs
Obin Kwon, Sankalp Yamsani, Noboru Myers, Sean Taylor, Jooyoung Hong, Kyungseo Park, Alex Alspach, Joohyung Kim
https://arxiv.org/abs/2507.05555

PAPRLE (Plug-And-Play Robotic Limb Environment): A Modular Ecosystem for Robotic Limbs
We introduce PAPRLE (Plug-And-Play Robotic Limb Environment), a modular ecosystem that enables flexible placement and control of robotic limbs. With PAPRLE, a user can change the arrangement of the robotic limbs, and control them using a variety of input devices, including puppeteers, gaming controllers, and VR-based interfaces. This versatility supports a wide range of teleoperation scenarios and promotes adaptability to different task requirements. To further enhance configurability, we intro…

Towards End-to-End Earthquake Monitoring Using a Multitask Deep Learning Model
Weiqiang Zhu, Junhao Song, Haoyu Wang, Jannes M\"unchmeyer
https://arxiv.org/abs/2506.06939

Towards End-to-End Earthquake Monitoring Using a Multitask Deep Learning Model
Seismic waveforms contain rich information about earthquake processes, making effective data analysis crucial for earthquake monitoring, source characterization, and seismic hazard assessment. With rapid developments in deep learning, the state-of-the-art approach in artificial intelligence, many neural network models have been developed to enhance earthquake monitoring tasks, such as earthquake detection, phase picking, and phase association. However, most current efforts focus on developing s…

Data Diversification Methods In Alignment Enhance Math Performance In LLMs
Berkan Dokmeci, Qingyang Wu, Ben Athiwaratkun, Ce Zhang, Shuaiwen Leon Song, James Zou
https://arxiv.org/abs/2507.02173

Data Diversification Methods In Alignment Enhance Math Performance In LLMs
While recent advances in preference learning have enhanced alignment in human feedback, mathematical reasoning remains a persistent challenge. We investigate how data diversification strategies in preference optimization can improve the mathematical reasoning abilities of large language models (LLMs). We evaluate three common data generation methods: temperature sampling, Chain-of-Thought prompting, and Monte Carlo Tree Search (MCTS), and introduce Diversified-ThinkSolve (DTS), a novel structur…

The Case for Instance-Optimized LLMs in OLAP Databases
Bardia Mohammadi, Laurent Bindschaedler
https://arxiv.org/abs/2507.04967 https://

The Case for Instance-Optimized LLMs in OLAP Databases
Large Language Models (LLMs) can enhance analytics systems with powerful data summarization, cleaning, and semantic transformation capabilities. However, deploying LLMs at scale -- processing millions to billions of rows -- remains prohibitively expensive in computation and memory. We present IOLM-DB, a novel system that makes LLM-enhanced database queries practical through query-specific model optimization. Instead of using general-purpose LLMs, IOLM-DB generates lightweight, specialized model…

From ID-based to ID-free: Rethinking ID Effectiveness in Multimodal Collaborative Filtering Recommendation
Guohao Li, Li Jing, Jia Wu, Xuefei Li, Kai Zhu, Yue He
https://arxiv.org/abs/2507.05715

From ID-based to ID-free: Rethinking ID Effectiveness in Multimodal Collaborative Filtering Recommendation
Most existing multimodal collaborative filtering recommendation (MCFRec) methods rely heavily on ID features and multimodal content to enhance recommendation performance. However, this paper reveals that ID features are effective but have limited benefits in multimodal collaborative filtering recommendation. Therefore, this paper systematically deconstruct the pros and cons of ID features: (i) they provide initial embedding but lack semantic richness, (ii) they provide a unique identifier for e…

Exact and Heuristic Algorithms for Constrained Biclustering
Antonio M. Sudoso
https://arxiv.org/abs/2508.05493 https://arxiv.org/pdf/2508.05493

Exact and Heuristic Algorithms for Constrained Biclustering
Biclustering, also known as co-clustering or two-way clustering, simultaneously partitions the rows and columns of a data matrix to reveal submatrices with coherent patterns. Incorporating background knowledge into clustering to enhance solution quality and interpretability has attracted growing interest in mathematical optimization and machine learning research. Extending this paradigm to biclustering enables prior information to guide the joint grouping of rows and columns. We study constrain…

OPTIMUMP2P: Fast and Reliable Gossiping in P2P Networks
Nicolas Nicolaou, Onyeka Obi, Aayush Rajasekaran, Alejandro Bergasov, Aleksandr Bezobchuk, Kishori M. Konwar, Michael Meier, Santiago Paiva, Har Preet Singh, Swarnabha Sinha
https://arxiv.org/abs/2508.04833

OPTIMUMP2P: Fast and Reliable Gossiping in P2P Networks
Gossip algorithms are pivotal in the dissemination of information within decentralized systems. Consequently, numerous gossip libraries have been developed and widely utilized especially in blockchain protocols for the propagation of blocks and transactions. A well-established library is libp2p, which provides two gossip algorithms: floodsup and gossibsup. These algorithms enable the delivery of published messages to a set of peers. In this work we aim to enhance the performance and reliability…

This https://arxiv.org/abs/2410.18239 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

E2E-Swin-Unet++: An Enhanced End-to-End Swin-Unet Architecture With Dual Decoders For PTMC Segmentation
Precise segmentation of papillary thyroid microcarcinoma (PTMC) during ultrasound-guided radiofrequency ablation (RFA) is critical for effective treatment but remains challenging due to acoustic artifacts, small lesion size, and anatomical variability. In this study, we propose \textbf{DualSwinUnet++}, a dual-decoder transformer-based architecture designed to enhance PTMC segmentation by incorporating thyroid gland context. DualSwinUnet++ employs independent linear projection heads for each dec…

Angular Blog editor Minko Gechev shares what's new on Angular 20. Highlights include newly stabilized APIs, improved debugging tools, polishing the developer experience, and features to enhance generative AI development.
Also, the Angular team has launched an RFC to pick an official mascot for Angular. Isn't that great?
"Announcing Angular v20"

Federated Learning-based MARL for Strengthening Physical-Layer Security in B5G Networks
Deemah H. Tashman, Soumaya Cherkaoui, Walaa Hamouda
https://arxiv.org/abs/2507.06997

Federated Learning-based MARL for Strengthening Physical-Layer Security in B5G Networks
This paper explores the application of a federated learning-based multi-agent reinforcement learning (MARL) strategy to enhance physical-layer security (PLS) in a multi-cellular network within the context of beyond 5G networks. At each cell, a base station (BS) operates as a deep reinforcement learning (DRL) agent that interacts with the surrounding environment to maximize the secrecy rate of legitimate users in the presence of an eavesdropper. This eavesdropper attempts to intercept the confid…

Joint User Association and Beamforming Design for ISAC Networks with Large Language Models
Haoyun Li, Ming Xiao, Kezhi Wang, Robert Schober, Dong In Kim, Yong Liang Guan
https://arxiv.org/abs/2506.05637

Joint User Association and Beamforming Design for ISAC Networks with Large Language Models
Integrated sensing and communication (ISAC) has been envisioned to play a more important role in future wireless networks. However, the design of ISAC networks is challenging, especially when there are multiple communication and sensing (C\&S) nodes and multiple sensing targets. We investigate a multi-base station (BS) ISAC network in which multiple BSs equipped with multiple antennas simultaneously provide C\&S services for multiple ground communication users (CUs) and targets. To enhance the …

Robust Power System State Estimation using Physics-Informed Neural Networks
Solon Falas, Markos Asprou, Charalambos Konstantinou, Maria K. Michael
https://arxiv.org/abs/2507.05874

Robust Power System State Estimation using Physics-Informed Neural Networks
Modern power systems face significant challenges in state estimation and real-time monitoring, particularly regarding response speed and accuracy under faulty conditions or cyber-attacks. This paper proposes a hybrid approach using physics-informed neural networks (PINNs) to enhance the accuracy and robustness, of power system state estimation. By embedding physical laws into the neural network architecture, PINNs improve estimation accuracy for transmission grid applications under both normal …

Learning Deliberately, Acting Intuitively: Unlocking Test-Time Reasoning in Multimodal LLMs
Yahan Yu, Yuyang Dong, Masafumi Oyamada
https://arxiv.org/abs/2507.06999

Learning Deliberately, Acting Intuitively: Unlocking Test-Time Reasoning in Multimodal LLMs
Reasoning is a key capability for large language models (LLMs), particularly when applied to complex tasks such as mathematical problem solving. However, multimodal reasoning research still requires further exploration of modality alignment and training costs. Many of these approaches rely on additional data annotation and relevant rule-based rewards to enhance the understanding and reasoning ability, which significantly increases training costs and limits scalability. To address these challeng…

Deep reinforcement learning-based joint real-time energy scheduling for green buildings with heterogeneous battery energy storage devices
Chi Liu, Zhezhuang Xu, Jiawei Zhou, Yazhou Yuan, Kai Ma, Meng Yuan
https://arxiv.org/abs/2506.06824

Deep reinforcement learning-based joint real-time energy scheduling for green buildings with heterogeneous battery energy storage devices
Green buildings (GBs) with renewable energy and building energy management systems (BEMS) enable efficient energy use and support sustainable development. Electric vehicles (EVs), as flexible storage resources, enhance system flexibility when integrated with stationary energy storage systems (ESS) for real-time scheduling. However, differing degradation and operational characteristics of ESS and EVs complicate scheduling strategies. This paper proposes a model-free deep reinforcement learning (…

This https://arxiv.org/abs/2504.21422 has been replaced.
initial toot: https://mastoxiv.page/@a…

Thermodynamics versus Coherence in Ultra Narrow Linewidth Optical Solid State Emitters
The coherence properties of optical emitters in crystals are crucial for quantum technologies and optical frequency metrology. Cooling to sub-kelvin temperatures can markedly enhance coherence, making it important to identify the parameters governing emitter and host crystal behavior in this regime. We investigate a Czochralski-grown europium-doped yttrium orthosilicate crystal, reporting measurements of its heat capacity and optical coherence. Heat capacity not only informs thermal noise limit…

This https://arxiv.org/abs/2409.05101 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

On the Need to Monitor Continuous Integration Practices -- An Empirical Study
Continuous Integration (CI) encompasses a set of widely adopted practices that enhance software development. However, there are indications that developers may not adequately monitor CI practices. Hence, this paper explores developers' perceptions regarding the monitoring CI practices. To achieve this, we first perform a Document Analysis to assess developers' expressed need for practice monitoring in pull requests comments generated by developers during the development process. After that, we …

Design Tradeoffs in Photonically Linked Qubit Networks
Ely Novakoski, Jungsang Kim
https://arxiv.org/abs/2506.06268 https://arxiv.org…

Design Tradeoffs in Photonically Linked Qubit Networks
Quantum networking can be realized by distributing pairs of entangled qubits between remote quantum processing nodes. Devoted communication qubits within each node can naturally interface with photons which bus quantum information between nodes. With the introduction of CQED to enhance interactions between communication qubits and photons, advanced protocols capable of achieving high entanglement distribution rates with high fidelity become feasible. In this paper, we consider two such protocol…

This https://arxiv.org/abs/2504.13398 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Insecurity Through Obscurity: Veiled Vulnerabilities in Closed-Source Contracts
Most blockchains cannot hide the binary code of programs (i.e., smart contracts) running on them. To conceal proprietary business logic and to potentially deter attacks, many smart contracts are closed-source and employ layers of obfuscation. However, we demonstrate that such obfuscation can obscure critical vulnerabilities rather than enhance security, a phenomenon we term insecurity through obscurity. To systematically analyze these risks on a large scale, we present SKANF, a novel EVM byteco…

This https://arxiv.org/abs/2504.12941 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Osmolyte-Modulated Differential Capacitance and Disjoining Pressure for Nanoconfined Electrolytes: A Modified Poisson-Boltzmann Theory
This study employs modified Poisson-Boltzmann theory to systematically investigate the influence of zwitterionic osmolyte additives to an electrolyte solution on disjoining pressure and electric differential capacitance within charged slit-like nanopores with conductive walls. We demonstrate that increasing concentrations of zwitterionic osmolytes synergistically enhance both disjoining pressure and differential capacitance, highlighting their dual role in the potential improvement of supercapa…

Harmony-Aware Music-driven Motion Synthesis with Perceptual Constraint on UGC Datasets
Xinyi Wu, Haohong Wang, Aggelos K. Katsaggelos
https://arxiv.org/abs/2506.07076

Harmony-Aware Music-driven Motion Synthesis with Perceptual Constraint on UGC Datasets
With the popularity of video-based user-generated content (UGC) on social media, harmony, as dictated by human perceptual principles, is critical in assessing the rhythmic consistency of audio-visual UGCs for better user engagement. In this work, we propose a novel harmony-aware GAN framework, following a specifically designed harmony evaluation strategy to enhance rhythmic synchronization in the automatic music-to-motion synthesis using a UGC dance dataset. This harmony strategy utilizes refin…

This https://arxiv.org/abs/2309.05677 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Optical trapping based on microring resonators with transverse slot structure
Over the past few decades, optical manipulation has emerged as a highly successful tool in various fields, such as biology, micro/nanorobotics, and physics. Among the different techniques, the transverse slot optical waveguide has shown remarkable potential in enhancing the field and significantly improving optical trapping capabilities. Additionally, microring resonators have demonstrated the ability to enhance the field at specific resonance wavelengths, enabling the manipulation and capture …

GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models
Zhen Yang, Haitao Lin, Jiawei xue, Ziji Zhang
https://arxiv.org/abs/2507.06507

GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models
In the past year, Generative Recommendations (GRs) have undergone substantial advancements, especially in leveraging the powerful sequence modeling and reasoning capabilities of Large Language Models (LLMs) to enhance overall recommendation performance. LLM-based GRs are forming a new paradigm that is distinctly different from discriminative recommendations, showing strong potential to replace traditional recommendation systems heavily dependent on complex hand-crafted features. In this paper, …

This https://arxiv.org/abs/2305.12844 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

An Optimized Ensemble Deep Learning Model For Brain Tumor Classification
Brain tumors present a grave risk to human life, demanding precise and timely diagnosis for effective treatment. Inaccurate identification of brain tumors can significantly diminish life expectancy, underscoring the critical need for precise diagnostic methods. Manual identification of brain tumors within vast Magnetic Resonance Imaging (MRI) image datasets is arduous and time-consuming. Thus, the development of a reliable deep learning (DL) model is essential to enhance diagnostic accuracy and…

MathSmith: Towards Extremely Hard Mathematical Reasoning by Forging Synthetic Problems with a Reinforced Policy
Shaoxiong Zhan, Yanlin Lai, Ziyu Lu, Dahua Lin, Ziqing Yang, Fei Tang
https://arxiv.org/abs/2508.05592

MathSmith: Towards Extremely Hard Mathematical Reasoning by Forging Synthetic Problems with a Reinforced Policy
Large language models have achieved substantial progress in mathematical reasoning, yet their advancement is limited by the scarcity of high-quality, high-difficulty training data. Existing synthesis methods largely rely on transforming human-written templates, limiting both diversity and scalability. We propose MathSmith, a novel framework for synthesizing challenging mathematical problems to enhance LLM reasoning. Rather than modifying existing problems, MathSmith constructs new ones from scr…

Enhancing PySCF-based Quantum Chemistry Simulations with Modern Hardware, Algorithms, and Python Tools
Zhichen Pu, Qiming Sun
https://arxiv.org/abs/2506.06661

Enhancing PySCF-based Quantum Chemistry Simulations with Modern Hardware, Algorithms, and Python Tools
The PySCF package has emerged as a powerful and flexible open-source platform for quantum chemistry simulations. However, the efficiency of electronic structure calculations can vary significantly depending on the choice of computational techniques and hardware utilization. In this paper, we explore strategies to enhance research productivity and computational performance in PySCF-based simulations. First, we discuss GPU acceleration for selected PySCF modules. Second, we demonstrate algorithmi…

Beyond Surface Similarity: Evaluating LLM-Based Test Refactorings with Structural and Semantic Awareness
Wendk\^uuni C. Ou\'edraogo, Yinghua Li, Xueqi Dang, Xin Zhou, Anil Koyuncu, Jacques Klein, David Lo, Tegawend\'e F. Bissyand\'e
https://arxiv.org/abs/2506.06767

Beyond Surface Similarity: Evaluating LLM-Based Test Refactorings with Structural and Semantic Awareness
Large Language Models (LLMs) are increasingly employed to automatically refactor unit tests, aiming to enhance readability, naming, and structural clarity while preserving functional behavior. However, evaluating such refactorings remains challenging: traditional metrics like CodeBLEU are overly sensitive to renaming and structural edits, whereas embedding-based similarities capture semantics but ignore readability and modularity. We introduce CTSES, a composite metric that integrates CodeBLEU,…

How the Kerr-Cat Qubit Dies-And How to Rescue It
Othmane Benhayoune-Khadraoui, Crist\'obal Lled\'o, Alexandre Blais
https://arxiv.org/abs/2507.06160

How the Kerr-Cat Qubit Dies-And How to Rescue It
Kerr-cat qubits have been experimentally shown to exhibit a large noise bias, with one decay channel suppressed by several orders of magnitude. In superconducting implementations, increasing the microwave drive on the nonlinear oscillator that hosts the Kerr-cat qubit should, in principle, further enhance this bias. Instead, experiments reveal that above a critical drive amplitude the tunneling time - the less dominant decay channel - ceases to increase and even decreases. Here, we show that th…

Iterative Sparse Asymptotic Minimum Variance Based Channel Estimation in Fluid Antenna System
Zhen Chen, Jianqing Li, Xiu Yin Zhang, Kai-Kit Wong, Chan-Byoung Chae, Yangyang Zhang
https://arxiv.org/abs/2507.05625

Iterative Sparse Asymptotic Minimum Variance Based Channel Estimation in Fluid Antenna System
With fluid antenna system (FAS) gradually establishing itself as a possible enabling technology for next generation wireless communications, channel estimation for FAS has become a pressing issue. Existing methodologies however face limitations in noise suppression. To overcome this, in this paper, we propose a maximum likelihood (ML)-based channel estimation approach tailored for FAS systems, designed to mitigate noise interference and enhance estimation accuracy. By capitalizing on the inhere…

Field-of-View and Input Constrained Impact Time Guidance Against Stationary Targets
Swati Singh, Shashi Ranjan Kumar, Dwaipayan Mukherjee
https://arxiv.org/abs/2506.05893

Field-of-View and Input Constrained Impact Time Guidance Against Stationary Targets
This paper proposes a guidance strategy to achieve time-constrained interception of stationary targets, taking into account both the bounded field-of-view (FOV) of seeker-equipped interceptors and the actuator's physical constraints. Actuator saturation presents a significant challenge in real-world systems, often resulting in degraded performance. However, since these limitations are typically known in advance, incorporating them into the guidance design can enhance overall performance. To add…

Common origin of the photoplastic and electroplastic effect in ZnS
Alexandra Fonseca Montenegro, Sevim Genlik Polat, Md Mohsinur Rahman Adnan, Maryam Ghazisaeidi, Roberto C. Myers
https://arxiv.org/abs/2506.07908

Common origin of the photoplastic and electroplastic effect in ZnS
Dislocation motion -- the atomic-scale mechanism of crystal plasticity -- governs the strength and ductility of materials. In functional materials, external stimuli beyond mechanical stress can also affect dislocation glide. In the wide band gap semiconductor ZnS, optical illumination suppresses plasticity, whereas electric fields can enhance dislocation motion. Here, we show that the common underlying mechanism for these phenomena is the charged dislocations that respond to the changes in carr…

AI Literacy and LLM Engagement in Higher Education: A Cross-National Quantitative Study
Shahin Hossain, Shapla Khanam, Samaa Haniya, Nesma Ragab Nasr
https://arxiv.org/abs/2507.03020

AI Literacy and LLM Engagement in Higher Education: A Cross-National Quantitative Study
This study presents a cross-national quantitative analysis of how university students in the United States and Bangladesh interact with Large Language Models (LLMs). Based on an online survey of 318 students, results show that LLMs enhance access to information, improve writing, and boost academic performance. However, concerns about overreliance, ethical risks, and critical thinking persist. Guided by the AI Literacy Framework, Expectancy-Value Theory, and Biggs' 3P Model, the study finds that…

This https://arxiv.org/abs/2504.04178 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

MSL: Not All Tokens Are What You Need for Tuning LLM as a Recommender
Large language models (LLMs), known for their comprehension capabilities and extensive knowledge, have been increasingly applied to recommendation systems (RS). Given the fundamental gap between the mechanism of LLMs and the requirement of RS, researchers have focused on fine-tuning LLMs with recommendation-specific data to enhance their performance. Language Modeling Loss (LML), originally designed for language generation tasks, is commonly adopted. However, we identify two critical limitation…

This https://arxiv.org/abs/2502.16212 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Clustering-Based Sub-QUBO Extraction for Hybrid QUBO Solvers
Quantum Approximate Optimization Algorithm (QAOA) can be used to solve quadratic unconstrained binary optimization (QUBO) problems. However, the size of the solvable problem is limited by the number of qubits. To leverage noisy intermediate-scale quantum (NISQ) devices to solve large-scale QUBO problems, one possible way is to decompose the full problem into multiple sub-problems, which we refer to as the Sub-QUBO Formalism. In this work, we enhance this formalism by proposing a sub-QUBO extrac…

This https://arxiv.org/abs/2312.17294 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub
Large Language Models (LLMs) excel in traditional natural language processing tasks but struggle with problems that require complex domain-specific calculations or simulations. While equipping LLMs with external tools to build LLM-based agents can enhance their capabilities, existing approaches lack the flexibility to address diverse and ever-evolving user queries in open domains. Currently, there is also no existing dataset that evaluates LLMs on open-domain knowledge that requires tools to so…

Robust adaptive fuzzy sliding mode control for trajectory tracking for of cylindrical manipulator
Van Cuong Pham, Minh Hai Tran, Phuc Anh Nguyen, Ngoc Son Vu, Nga Nguyen Thi
https://arxiv.org/abs/2508.05584

Robust adaptive fuzzy sliding mode control for trajectory tracking for of cylindrical manipulator
This research proposes a robust adaptive fuzzy sliding mode control (AFSMC) approach to enhance the trajectory tracking performance of cylindrical robotic manipulators, extensively utilized in applications such as CNC and 3D printing. The proposed approach integrates fuzzy logic with sliding mode control (SMC) to bolster adaptability and robustness, with fuzzy logic approximating the uncertain dynamics of the system, while SMC ensures strong performance. Simulation results in MATLAB/Simulink de…

GradOT: Training-free Gradient-preserving Offsite-tuning for Large Language Models
Kai Yao, Zhaorui Tan, Penglei Gao, Lichun Li, Kaixin Wu, Yinggui Wang, Yuan Zhao, Yixin Ji, Wei Wang, Jianke Zhu
https://arxiv.org/abs/2507.04455

GradOT: Training-free Gradient-preserving Offsite-tuning for Large Language Models
The rapid growth of large language models (LLMs) with traditional centralized fine-tuning emerges as a key technique for adapting these models to domain-specific challenges, yielding privacy risks for both model and data owners. One promising solution, called offsite-tuning (OT), is proposed to address these challenges, where a weaker emulator is compressed from the original model and further fine-tuned with adapter to enhance privacy. However, the existing OT-based methods require high computa…

This https://arxiv.org/abs/2504.14522 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Biased by Design: Leveraging AI Inherent Biases to Enhance Critical Thinking of News Readers
This paper explores the design of a propaganda detection tool using Large Language Models (LLMs). Acknowledging the inherent biases in AI models, especially in political contexts, we investigate how these biases might be leveraged to enhance critical thinking in news consumption. Countering the typical view of AI biases as detrimental, our research proposes strategies of user choice and personalization in response to a user's political stance, applying psychological concepts of confirmation bia…

This https://arxiv.org/abs/2410.20580 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Coherence-guided Preference Disentanglement for Cross-domain Recommendations
Discovering user preferences across different domains is pivotal in cross-domain recommendation systems, particularly when platforms lack comprehensive user-item interactive data. The limited presence of shared users often hampers the effective modeling of common preferences. While leveraging shared items' attributes, such as category and popularity, can enhance cross-domain recommendation performance, the scarcity of shared items between domains has limited research in this area. To address th…

Uni-cot: Towards Unified Chain-of-Thought Reasoning Across Text and Vision
Luozheng Qin, Jia Gong, Yuqing Sun, Tianjiao Li, Mengping Yang, Xiaomeng Yang, Chao Qu, Zhiyu Tan, Hao Li
https://arxiv.org/abs/2508.05606

Uni-cot: Towards Unified Chain-of-Thought Reasoning Across Text and Vision
Chain-of-Thought (CoT) reasoning has been widely adopted to enhance Large Language Models (LLMs) by decomposing complex tasks into simpler, sequential subtasks. However, extending CoT to vision-language reasoning tasks remains challenging, as it often requires interpreting transitions of visual states to support reasoning. Existing methods often struggle with this due to limited capacity of modeling visual state transitions or incoherent visual trajectories caused by fragmented architectures. …

Study of AP Association and Users and Power Allocation for Cell-Free Massive MIMO Systems
S. Mohammadzadeh, S. Mashdour, R. C. de Lamare, K. Cumanan, C. Li
https://arxiv.org/abs/2507.03848

Study of AP Association and Users and Power Allocation for Cell-Free Massive MIMO Systems
This paper introduces an access point-user (AP-UE) association strategy combined with pilot power allocation to mitigate multiuser interference and enhance spectral efficiency (SE) in clustered cell-free massive MIMO (CCF-mMIMO) networks. We propose a dynamic channel-based clustering method that groups APs according to their channel correlation, ensuring users are associated with APs exhibiting similar channel characteristics. The proposed approach exploits hierarchical clustering, enabling fle…

Perspectives on How Sociology Can Advance Theorizing about Human-Chatbot Interaction and Developing Chatbots for Social Good
Celeste Campos-Castillo, Xuan Kang, Linnea I. Laestadius
https://arxiv.org/abs/2507.05030

Perspectives on How Sociology Can Advance Theorizing about Human-Chatbot Interaction and Developing Chatbots for Social Good
Recently, research into chatbots (also known as conversational agents, AI agents, voice assistants), which are computer applications using artificial intelligence to mimic human-like conversation, has grown sharply. Despite this growth, sociology lags other disciplines (including computer science, medicine, psychology, and communication) in publishing about chatbots. We suggest sociology can advance understanding of human-chatbot interaction and offer four sociological theories to enhance extan…

Extreme Learning Machine Based System for DDoS Attacks Detections on IoMT Devices
Nelly Elsayed, Lily Dzamesi, Zag ElSayed, Murat Ozer
https://arxiv.org/abs/2507.05132

Extreme Learning Machine Based System for DDoS Attacks Detections on IoMT Devices
The Internet of Medical Things (IoMT) represents a paradigm shift in the healthcare sector, enabling the interconnection of medical devices, sensors, and systems to enhance patient monitoring, diagnosis, and management. The rapid evolution of IoMT presents significant benefits to the healthcare domains. However, there is a rapid increase in distributed denial of service (DDoS) attacks on the IoMT networks due to several vulnerabilities in the IoMT-connected devices, which negatively impact pati…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[5/5]:
- STF: Shallow-Level Temporal Feedback to Enhance Spiking Transformers
Zheng, Zhu, Yu, Huang, Lv, Tang, Yu, Jin

ReFuzzer: Feedback-Driven Approach to Enhance Validity of LLM-Generated Test Programs
Iti Shree, Karine Even-Mendoz, Tomasz Radzik
https://arxiv.org/abs/2508.03603 https://

ReFuzzer: Feedback-Driven Approach to Enhance Validity of LLM-Generated Test Programs
Existing LLM-based compiler fuzzers often produce syntactically or semantically invalid test programs, limiting their effectiveness in exercising compiler optimizations and backend components. We introduce ReFuzzer, a framework for refining LLM-generated test programs by systematically detecting and correcting compilation and runtime violations (e.g. division by zero or array out-of-bounds accesses). ReFuzzer employs a feedback loop with a local LLM to validate and filter erroneous programs bef…

Estimating Object Physical Properties from RGB-D Vision and Depth Robot Sensors Using Deep Learning
Ricardo Cardoso, Plinio Moreno
https://arxiv.org/abs/2507.05029

Estimating Object Physical Properties from RGB-D Vision and Depth Robot Sensors Using Deep Learning
Inertial mass plays a crucial role in robotic applications such as object grasping, manipulation, and simulation, providing a strong prior for planning and control. Accurately estimating an object's mass before interaction can significantly enhance the performance of various robotic tasks. However, mass estimation using only vision sensors is a relatively underexplored area. This paper proposes a novel approach combining sparse point-cloud data from depth images with RGB images to estimate the …

Unveiling Privacy Policy Complexity: An Exploratory Study Using Graph Mining, Machine Learning, and Natural Language Processing
Vijayalakshmi Ramasamy, Seth Barrett, Gokila Dorai, Jessica Zumbach
https://arxiv.org/abs/2507.02968

Unveiling Privacy Policy Complexity: An Exploratory Study Using Graph Mining, Machine Learning, and Natural Language Processing
Privacy policy documents are often lengthy, complex, and difficult for non-expert users to interpret, leading to a lack of transparency regarding the collection, processing, and sharing of personal data. As concerns over online privacy grow, it is essential to develop automated tools capable of analyzing privacy policies and identifying potential risks. In this study, we explore the potential of interactive graph visualizations to enhance user understanding of privacy policies by representing p…

CTA: Cross-Task Alignment for Better Test Time Training
Samuel Barbeau, Pedram Fekri, David Osowiechi, Ali Bahri, Moslem YazdanpanahMasih Aminbeidokhti, Christian Desrosiers
https://arxiv.org/abs/2507.05221

CTA: Cross-Task Alignment for Better Test Time Training
Deep learning models have demonstrated exceptional performance across a wide range of computer vision tasks. However, their performance often degrades significantly when faced with distribution shifts, such as domain or dataset changes. Test-Time Training (TTT) has emerged as an effective method to enhance model robustness by incorporating an auxiliary unsupervised task during training and leveraging it for model updates at test time. In this work, we introduce CTA (Cross-Task Alignment), a nov…

Reason-to-Recommend: Using Interaction-of-Thought Reasoning to Enhance LLM Recommendation
Keyu Zhao, Fengli Xu, Yong Li
https://arxiv.org/abs/2506.05069 ht…

Reason-to-Recommend: Using Interaction-of-Thought Reasoning to Enhance LLM Recommendation
Driven by advances in Large Language Models (LLMs), integrating them into recommendation tasks has gained interest due to their strong semantic understanding and prompt flexibility. Prior work encoded user-item interactions or metadata into prompts for recommendations. In parallel, LLM reasoning, boosted by test-time scaling and reinforcement learning, has excelled in fields like mathematics and code, where reasoning traces and correctness signals are clear, enabling high performance and interp…

Can Video LLMs Refuse to Answer? Alignment for Answerability in Video Large Language Models
Eunseop Yoon, Hee Suk Yoon, Mark A. Hasegawa-Johnson, Chang D. Yoo
https://arxiv.org/abs/2507.04976

Can Video LLMs Refuse to Answer? Alignment for Answerability in Video Large Language Models
In the broader context of deep learning, Multimodal Large Language Models have achieved significant breakthroughs by leveraging powerful Large Language Models as a backbone to align different modalities into the language space. A prime exemplification is the development of Video Large Language Models (Video-LLMs). While numerous advancements have been proposed to enhance the video understanding capabilities of these models, they are predominantly trained on questions generated directly from vid…

Function-based Labels for Complementary Recommendation: Definition, Annotation, and LLM-as-a-Judge
Chihiro Yamasaki, Kai Sugahara, Yuma Nagi, Kazushi Okamoto
https://arxiv.org/abs/2507.03945

Function-based Labels for Complementary Recommendation: Definition, Annotation, and LLM-as-a-Judge
Complementary recommendations enhance the user experience by suggesting items that are frequently purchased together while serving different functions from the query item. Inferring or evaluating whether two items have a complementary relationship requires complementary relationship labels; however, defining these labels is challenging because of the inherent ambiguity of such relationships. Complementary labels based on user historical behavior logs attempt to capture these relationships, but …

Differential Attention for Multimodal Crisis Event Analysis
Nusrat Munia, Junfeng Zhu, Olfa Nasraoui, Abdullah-Al-Zubaer Imran
https://arxiv.org/abs/2507.05165

Differential Attention for Multimodal Crisis Event Analysis
Social networks can be a valuable source of information during crisis events. In particular, users can post a stream of multimodal data that can be critical for real-time humanitarian response. However, effectively extracting meaningful information from this large and noisy data stream and effectively integrating heterogeneous data remains a formidable challenge. In this work, we explore vision language models (VLMs) and advanced fusion strategies to enhance the classification of crisis data in…

Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations
Xiang Xu, Lingdong Kong, Song Wang, Chuanwei Zhou, Qingshan Liu
https://arxiv.org/abs/2507.05260

Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations
LiDAR representation learning aims to extract rich structural and semantic information from large-scale, readily available datasets, reducing reliance on costly human annotations. However, existing LiDAR representation strategies often overlook the inherent spatiotemporal cues in LiDAR sequences, limiting their effectiveness. In this work, we propose LiMA, a novel long-term image-to-LiDAR Memory Aggregation framework that explicitly captures longer range temporal correlations to enhance LiDAR r…

TayFCS: Towards Light Feature Combination Selection for Deep Recommender Systems
Xianquan Wang, Zhaocheng Du, Jieming Zhu, Chuhan Wu, Qinglin Jia, Zhenhua Dong
https://arxiv.org/abs/2507.03895

TayFCS: Towards Light Feature Combination Selection for Deep Recommender Systems
Feature interaction modeling is crucial for deep recommendation models. A common and effective approach is to construct explicit feature combinations to enhance model performance. However, in practice, only a small fraction of these combinations are truly informative. Thus it is essential to select useful feature combinations to reduce noise and manage memory consumption. While feature selection methods have been extensively studied, they are typically limited to selecting individual features. …

Do We Really Need Specialization? Evaluating Generalist Text Embeddings for Zero-Shot Recommendation and Search
Matteo Attimonelli, Alessandro De Bellis, Claudio Pomo, Dietmar Jannach, Eugenio Di Sciascio, Tommaso Di Noia
https://arxiv.org/abs/2507.05006

Do We Really Need Specialization? Evaluating Generalist Text Embeddings for Zero-Shot Recommendation and Search
Pre-trained language models (PLMs) are widely used to derive semantic representations from item metadata in recommendation and search. In sequential recommendation, PLMs enhance ID-based embeddings through textual metadata, while in product search, they align item characteristics with user intent. Recent studies suggest task and domain-specific fine-tuning are needed to improve representational power. This paper challenges this assumption, showing that Generalist Text Embedding Models (GTEs), p…

Multi-Modal Multi-Behavior Sequential Recommendation with Conditional Diffusion-Based Feature Denoising
Xiaoxi Cui, Weihai Lu, Yu Tong, Yiheng Li, Zhejun Zhao
https://arxiv.org/abs/2508.05352

Multi-Modal Multi-Behavior Sequential Recommendation with Conditional Diffusion-Based Feature Denoising
The sequential recommendation system utilizes historical user interactions to predict preferences. Effectively integrating diverse user behavior patterns with rich multimodal information of items to enhance the accuracy of sequential recommendations is an emerging and challenging research direction. This paper focuses on the problem of multi-modal multi-behavior sequential recommendation, aiming to address the following challenges: (1) the lack of effective characterization of modal preferences…

CTR-Guided Generative Query Suggestion in Conversational Search
Erxue Min, Hsiu-Yuan Huang, Xihong Yang, Min Yang, Xin Jia, Yunfang Wu, Hengyi Cai, Junfeng Wang, Shuaiqiang Wang, Dawei Yin
https://arxiv.org/abs/2507.04072

CTR-Guided Generative Query Suggestion in Conversational Search
Generating effective query suggestions in conversational search requires aligning model outputs with user preferences, which is challenging due to sparse and noisy click signals. We propose GQS, a generative framework that integrates click modeling and preference optimization to enhance real-world user engagement. GQS consists of three key components: (1) a Multi-Source CTR Modeling module that captures diverse contextual signals to estimate fine-grained click-through rates; (2) a Diversity-Awa…