Tootfinder

Transform-Resampled Double Bootstrap Percentile with Applications in System Reliability Assessment
Junpeng Gong, Xu He, Zhaohui Li
https://arxiv.org/abs/2506.04573

Transform-Resampled Double Bootstrap Percentile with Applications in System Reliability Assessment
System reliability assessment(SRA) is a challenging task due to the limited experimental data and the complex nature of the system structures. Despite a long history dating back to \cite{buehler1957confidence}, exact methods have only been applied to SRA for simple systems. High-order asymptotic methods, such as the Cornish-Fisher expansion, have become popular for balancing computational efficiency with improved accuracy when data are limited, but frequently encounter the "bend-back" problem i…

STRATUS: A Multi-agent System for Autonomous Reliability Engineering of Modern Clouds
Yinfang Chen, Jiaqi Pan, Jackson Clark, Yiming Su, Noah Zheutlin, Bhavya Bhavya, Rohan Arora, Yu Deng, Saurabh Jha, Tianyin Xu
https://arxiv.org/abs/2506.02009

STRATUS: A Multi-agent System for Autonomous Reliability Engineering of Modern Clouds
In cloud-scale systems, failures are the norm. A distributed computing cluster exhibits hundreds of machine failures and thousands of disk failures; software bugs and misconfigurations are reported to be more frequent. The demand for autonomous, AI-driven reliability engineering continues to grow, as existing humanin-the-loop practices can hardly keep up with the scale of modern clouds. This paper presents STRATUS, an LLM-based multi-agent system for realizing autonomous Site Reliability Engine…

KPIRoot : An Efficient Integrated Framework for Anomaly Detection and Root Cause Analysis in Large-Scale Cloud Systems
Wenwei Gu, Renyi Zhong, Guangba Yu, Xinying Sun, Jinyang Liu, Yintong Huo, Zhuangbin Chen, Jianping Zhang, Jiazhen Gu, Yongqiang Yang, Michael R. Lyu
https://arxiv.org/abs/2506.04569

KPIRoot+: An Efficient Integrated Framework for Anomaly Detection and Root Cause Analysis in Large-Scale Cloud Systems
To ensure the reliability of cloud systems, their performance is monitored using KPIs (key performance indicators). When issues arise, root cause localization identifies KPIs responsible for service degradation, aiding in quick diagnosis and resolution. Traditional methods rely on similarity calculations, which can be ineffective in complex, interdependent cloud environments. While deep learning-based approaches model these dependencies better, they often face challenges such as high computatio…

Facts are Harder Than Opinions -- A Multilingual, Comparative Analysis of LLM-Based Fact-Checking Reliability
Lorraine Saju, Arnim Bleier, Jana Lasser, Claudia Wagner
https://arxiv.org/abs/2506.03655

Facts are Harder Than Opinions -- A Multilingual, Comparative Analysis of LLM-Based Fact-Checking Reliability
The proliferation of misinformation necessitates scalable, automated fact-checking solutions. Yet, current benchmarks often overlook multilingual and topical diversity. This paper introduces a novel, dynamically extensible data set that includes 61,514 claims in multiple languages and topics, extending existing datasets up to 2024. Through a comprehensive evaluation of five prominent Large Language Models (LLMs), including GPT-4o, GPT-3.5 Turbo, LLaMA 3.1, and Mixtral 8x7B, we identify signific…

This https://arxiv.org/abs/2409.17800 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Bias Assessment and Data Drift Detection in Medical Image Analysis: A Survey
Machine Learning (ML) models have gained popularity in medical imaging analysis given their expert level performance in many medical domains. To enhance the trustworthiness, acceptance, and regulatory compliance of medical imaging models and to facilitate their integration into clinical settings, we review and categorise methods for ensuring ML reliability, both during development and throughout the model's lifespan. Specifically, we provide an overview of methods assessing models' inner-workin…

This https://arxiv.org/abs/2502.03278 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Fault-Tolerant Control for System Availability and Continuous Operation in Heavy-Duty Wheeled Mobile Robots
When the control system in a heavy-duty wheeled mobile robot (HD-WMR) malfunctions, deviations from ideal motion occur, significantly heightening the risks of off-road instability and costly damage. To meet the demands for safety, reliability, and controllability in HD-WMRs, the control system must tolerate faults to a certain extent, ensuring continuous operation. To this end, this paper introduces a model-free hierarchical control with fault accommodation (MFHCA) framework designed to address…

BEAR: BGP Event Analysis and Reporting
Hanqing Li, Melania Fedeli, Vinay Kolar, Diego Klabjan
https://arxiv.org/abs/2506.04514 https://

BEAR: BGP Event Analysis and Reporting
The Internet comprises of interconnected, independently managed Autonomous Systems (AS) that rely on the Border Gateway Protocol (BGP) for inter-domain routing. BGP anomalies--such as route leaks and hijacks--can divert traffic through unauthorized or inefficient paths, jeopardizing network reliability and security. Although existing rule-based and machine learning methods can detect these anomalies using structured metrics, they still require experts with in-depth BGP knowledge of, for example…

This https://arxiv.org/abs/2505.22990 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

MenTeR: A fully-automated Multi-agenT workflow for end-to-end RF/Analog Circuits Netlist Design
RF/Analog design is essential for bridging digital technologies with real-world signals, ensuring the functionality and reliability of a wide range of electronic systems. However, analog design procedures are often intricate, time-consuming and reliant on expert intuition, and hinder the time and cost efficiency of circuit development. To overcome the limitations of the manual circuit design, we introduce MenTeR - a multiagent workflow integrated into an end-to-end analog design framework. By e…

Multipurpose in situ cell design for 3D X-ray imaging of electrochemical processes
Riley J. Hultquist, David Simonne, Sayantan Mondal, Ericmoore Jossou
https://arxiv.org/abs/2506.04543

Multipurpose in situ cell design for 3D X-ray imaging of electrochemical processes
We present the design of a modular multipurpose cell for monitoring the degradation of materials in extreme environments. This cell decouples the reference electrode from the working and counter electrodes, permitting precise electrochemical control and measurement reliability. The design is compatible with 4th generation synchrotron light sources, and its emphasis on modularity facilitates adaptation to different beamlines, where there may be variations in sample stage requirements and X-ray i…

D-Rex: Heterogeneity-Aware Reliability Framework and Adaptive Algorithms for Distributed Storage
Maxime Gonthier (University of Chicago, Argonne National Laboratory), Dante D. Sanchez-Gallegos (Universidad Carlos III de Madrid), Haochen Pan (University of Chicago), Bogdan Nicolae (Argonne National Laboratory), Sicheng Zhou (Southern University of Science and Technology), Hai Duc Nguyen (University of Chicago, Argonne National Laboratory), Valerie Hayot-Sasson (University of Chicago, Ar…

D-Rex: Heterogeneity-Aware Reliability Framework and Adaptive Algorithms for Distributed Storage
The exponential growth of data necessitates distributed storage models, such as peer-to-peer systems and data federations. While distributed storage can reduce costs and increase reliability, the heterogeneity in storage capacity, I/O performance, and failure rates of storage resources makes their efficient use a challenge. Further, node failures are common and can lead to data unavailability and even data loss. Erasure coding is a common resiliency strategy implemented in storage systems to mi…

Reactive Transport Simulation of Silicate-Rich Shale Rocks when Exposed to CO2 Saturated Brine Under High Pressure and High Temperature
Shaziya A. Banu, Venkata R. S. B. Varanasi, Arash Noshadravan, Sara Abedi
https://arxiv.org/abs/2506.05122

Reactive Transport Simulation of Silicate-Rich Shale Rocks when Exposed to CO2 Saturated Brine Under High Pressure and High Temperature
This study examines the feasibility of carbon dioxide storage in shale rocks and the reliability of reactive transport models in achieving accurate replication of the chemo-mechanical interactions and transport processes transpiring in these rocks when subjected to CO2 saturated brine. Owing to the heterogeneity of rocks, experimental testing for adequate deductions and findings, could be an expensive and time-intensive process. Therefore, this study proposes utilization of reactive transport m…

This https://arxiv.org/abs/2505.22251 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Evaluation of LLMs in Speech is Often Flawed: Test Set Contamination in Large Language Models for Speech Recognition
Recent work suggests that large language models (LLMs) can improve performance of speech tasks compared to existing systems. To support their claims, results on LibriSpeech and Common Voice are often quoted. However, this work finds that a substantial amount of the LibriSpeech and Common Voice evaluation sets appear in public LLM pretraining corpora. This calls into question the reliability of findings drawn from these two datasets. To measure the impact of contamination, LLMs trained with or w…

Robust Estimation in Step-Stress Experiments under Exponential Lifetime Distributions
Mar\'ia Jaenada, Juan Manuel Mill\'an, Leandro Pardo
https://arxiv.org/abs/2506.04445

Robust Estimation in Step-Stress Experiments under Exponential Lifetime Distributions
Many modern products exhibit high reliability, often resulting in long times to failure. Consequently, conducting experiments under normal operating conditions may require an impractically long duration to obtain sufficient failure data for reliable statistical inference. As an alternative, accelerated life tests (ALTs) are employed to induce earlier failures and thereby reduce testing time. In step-stress experiments a stress factor that accelerates product degradation is identified and system…

On the Synthetic Channels in Polar Codes over Binary-Input Discrete Memoryless Channels
Yadong Jiao, Xiaoyan Cheng, Yuansheng Tang, Ming Xu
https://arxiv.org/abs/2506.04163

On the Synthetic Channels in Polar Codes over Binary-Input Discrete Memoryless Channels
Polar codes introduced by Arikan in 2009 are the first code family achieving the capacity of binary-input discrete memoryless channels (BIDMCs) with low-complexity encoding and decoding. Identifying unreliable synthetic channels in polar code construction is crucial. Currently, because of the large size of the output alphabets of synthetic channels, there is no effective approach to evaluate their reliability, except in the case that the underlying channels are binary erasure channels. This pap…

Knowledge-guided Contextual Gene Set Analysis Using Large Language Models
Zhizheng Wang, Chi-Ping Day, Chih-Hsuan Wei, Qiao Jin, Robert Leaman, Yifan Yang, Shubo Tian, Aodong Qiu, Yin Fang, Qingqing Zhu, Xinghua Lu, Zhiyong Lu
https://arxiv.org/abs/2506.04303

Knowledge-guided Contextual Gene Set Analysis Using Large Language Models
Gene set analysis (GSA) is a foundational approach for interpreting genomic data of diseases by linking genes to biological processes. However, conventional GSA methods overlook clinical context of the analyses, often generating long lists of enriched pathways with redundant, nonspecific, or irrelevant results. Interpreting these requires extensive, ad-hoc manual effort, reducing both reliability and reproducibility. To address this limitation, we introduce cGSA, a novel AI-driven framework tha…

This https://arxiv.org/abs/2503.00600 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Semantic Integrity Constraints: Declarative Guardrails for AI-Augmented Data Processing Systems
AI-augmented data processing systems (DPSs) integrate large language models (LLMs) into query pipelines, allowing powerful semantic operations on structured and unstructured data. However, the reliability (a.k.a. trust) of these systems is fundamentally challenged by the potential for LLMs to produce errors, limiting their adoption in critical domains. To help address this reliability bottleneck, we introduce semantic integrity constraints (SICs) -- a declarative abstraction for specifying and …

Indoor Sharing in the Mid-Band: A Performance Study of Neutral-Host, Cellular Macro, and Wi-Fi
Joshua Roy Palathinkal, Muhammad Iqbal Rochman, Vanlin Sathya, Mehmet Yavuz, Monisha Ghosh
https://arxiv.org/abs/2506.04974

Indoor Sharing in the Mid-Band: A Performance Study of Neutral-Host, Cellular Macro, and Wi-Fi
Indoor environments present a significant challenge for wireless connectivity, as immense data demand strains traditional solutions. Public Mobile Network Operators (MNOs), utilizing outdoor macro base stations (BSs), suffer from poor signal penetration. Indoor Wi-Fi networks, on the other hand, may face reliability issues due to spectrum contention. Shared spectrum models, particularly the Citizens Broadband Radio Service (CBRS) utilized by private 4G/5G networks, have emerged as a promising a…

This https://arxiv.org/abs/2412.06551 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Deterministic and randomized LU-Householder CholeskyQR
In this work, we develop LU-Householder CholeskyQR (LHC) for QR factorization of tall-skinny matrices. Similar to LU-CholeskyQR \cite{LUChol}, LHC does not require a sufficient condition of $κ_{2}(X)$ for the input tall-skinny matrix $X \in \mathbb{R}^{m\times n}$. This characteristic ensures the algorithm's reliability in the real-world applications, which is different from other CholeskyQR-type algorithms. To address the issue of numerical breakdown in Cholesky factorization of LU-CholeskyQR…

Blockchain Powered Edge Intelligence for U-Healthcare in Privacy Critical and Time Sensitive Environment
Anum Nawaz, Hafiz Humza Mahmood Ramzan, Xianjia Yu, Zhuo Zou, Tomi Westerlund
https://arxiv.org/abs/2506.02038

Blockchain Powered Edge Intelligence for U-Healthcare in Privacy Critical and Time Sensitive Environment
Edge Intelligence (EI) serves as a critical enabler for privacy-preserving systems by providing AI-empowered computation and distributed caching services at the edge, thereby minimizing latency and enhancing data privacy. The integration of blockchain technology further augments EI frameworks by ensuring transactional transparency, auditability, and system-wide reliability through a decentralized network model. However, the operational architecture of such systems introduces inherent vulnerabil…

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

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

Investigating the emergent invariant properties of Hungarian electric distribution networks
Michelle T. Cirunay, B\'alint Hartmann, T\'imea Erdei, Tam\'as Soha
https://arxiv.org/abs/2506.04009

Investigating the emergent invariant properties of Hungarian electric distribution networks
Electric power distribution networks serve as the final and essential stage in power delivery, bridging transmission infrastructure and end users. The structural configuration of these networks plays a critical role in determining system reliability, fault tolerance, and operational efficiency. Although the design of distribution systems is influenced by various regional factors, such as geography, customer density, and planning standards, the extent to which consistent structural characteristi…

This https://arxiv.org/abs/2505.04608 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales
Responsibly deploying artificial intelligence (AI) / machine learning (ML) systems in high-stakes settings arguably requires not only proof of system reliability, but also continual, post-deployment monitoring to quickly detect and address any unsafe behavior. Methods for nonparametric sequential testing -- especially conformal test martingales (CTMs) and anytime-valid inference -- offer promising tools for this monitoring task. However, existing approaches are restricted to monitoring limited …

Unicorn-CIM: Unconvering the Vulnerability and Improving the Resilience of High-Precision Compute-in-Memory
Qiufeng Li, Yiwen Liang, Weidong Cao
https://arxiv.org/abs/2506.02311

Unicorn-CIM: Unconvering the Vulnerability and Improving the Resilience of High-Precision Compute-in-Memory
Compute-in-memory (CIM) architecture has been widely explored to address the von Neumann bottleneck in accelerating deep neural networks (DNNs). However, its reliability remains largely understudied, particularly in the emerging domain of floating-point (FP) CIM, which is crucial for speeding up high-precision inference and on device training. This paper introduces Unicorn-CIM, a framework to uncover the vulnerability and improve the resilience of high-precision CIM, built on static r…

The World As Large Language Models See It: Exploring the reliability of LLMs in representing geographical features
Omid Reza Abbasi, Franz Welscher, Georg Weinberger, Johannes Scholz
https://arxiv.org/abs/2506.00203

The World As Large Language Models See It: Exploring the reliability of LLMs in representing geographical features
As large language models (LLMs) continue to evolve, questions about their trustworthiness in delivering factual information have become increasingly important. This concern also applies to their ability to accurately represent the geographic world. With recent advancements in this field, it is relevant to consider whether and to what extent LLMs' representations of the geographical world can be trusted. This study evaluates the performance of GPT-4o and Gemini 2.0 Flash in three key geospatial …

This https://arxiv.org/abs/2502.14814 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

VB-Com: Learning Vision-Blind Composite Humanoid Locomotion Against Deficient Perception
The performance of legged locomotion is closely tied to the accuracy and comprehensiveness of state observations. Blind policies, which rely solely on proprioception, are considered highly robust due to the reliability of proprioceptive observations. However, these policies significantly limit locomotion speed and often require collisions with the terrain to adapt. In contrast, Vision policies allows the robot to plan motions in advance and respond proactively to unstructured terrains with an o…

This https://arxiv.org/abs/2503.23604 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

The optimum mix of storage and backup in a highly renewable, highly reliable European electricity grid
We estimate the variability of solar and wind energy generation potential in Europe over a 43 year period between 1980-2022 with the MERRA-2 reanalysis datasets. We compare the estimated supply potential to hourly demand data from 36 European countries to calculate the reliability of a highly renewable electricity grid in Europe. We find that in cost-optimised scenarios with onshore wind, solar and storage, but no natural gas, reliably meeting the last 1% of demand represents 36% of the entire …

MRE stands for Meal Reliability Engineer

A novel stratified sampler with unbalanced refinement for network reliability assessment
Jianpeng Chan, Iason Papaioannou, Daniel Straub
https://arxiv.org/abs/2506.01044

A novel stratified sampler with unbalanced refinement for network reliability assessment
We investigate stratified sampling in the context of network reliability assessment. We propose an unbalanced stratum refinement procedure, which operates on a partition of network components into clusters and the number of failed components within each cluster. The size of each refined stratum and the associated conditional failure probability, collectively termed failure signatures, can be calculated and estimated using the conditional Bernoulli model. The estimator is further improved by det…

This https://arxiv.org/abs/2305.11943 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Public versus Less-Public News Engagement on Facebook: Patterns Across Bias and Reliability
The rapid growth of social media as a news platform has raised significant concerns about the influence and societal impact of biased and unreliable news on these platforms. While much research has explored user engagement with news on platforms like Facebook, most studies have focused on publicly shared posts. This focus leaves an important question unanswered: how representative is the public sphere of Facebook's entire ecosystem? Specifically, how much of the interactions occur in less-publi…

This https://arxiv.org/abs/2405.08719 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
Driven by steady progress in deep generative modeling, simulation-based inference (SBI) has emerged as the workhorse for inferring the parameters of stochastic simulators. However, recent work has demonstrated that model misspecification can compromise the reliability of SBI, preventing its adoption in important applications where only misspecified simulators are available. This work introduces robust posterior estimation~(RoPE), a framework that overcomes model misspecification with a small re…

A Reliable Vertical Federated Learning Framework for Traffic State Estimation with Data Selection and Incentive Mechanisms
Zijun Zhan, Yaxian Dong, Daniel Mawunyo Doe, Yuqing Hu, Shuai Li, Shaohua Cao, Zhu Han
https://arxiv.org/abs/2506.01285

A Reliable Vertical Federated Learning Framework for Traffic State Estimation with Data Selection and Incentive Mechanisms
Vertical Federated Learning (VFL)-based Traffic State Estimation (TSE) offers a promising approach for integrating vertically distributed traffic data from municipal authorities (MA) and mobility providers (MP) while safeguarding privacy. However, given the variations in MPs' data collection capabilities and the potential for MPs to underperform in data provision, we propose a reliable VFL-based TSE framework that ensures model reliability during training and operation. The proposed framework c…

This https://arxiv.org/abs/2409.18334 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Decade-long periodicity study of 2FHL blazars with historical optical data
In our recent investigation, we utilized a century's worth of archival optical data to search for a decade-long periodicity from the blazar PG 1553+113, finding a hint of a 22-yr period. Building on this foundation, the current study extends our analysis to include 10 blazars from the Fermi-Large Area Telescope Second Catalog of Hard Sources (2FHL) catalogue to uncover similar long-term periodic behaviour. To ensure the reliability of our findings, we consider the impact of observational limita…

"Politically, many diaspora Jews have felt increasingly homeless. The left, consumed by identity politics, often included them in the “white” majority and expected them to denounce any affiliation with Israel as proof of their ideological reliability. On the right, populist-nationalist parties, evolved from movements that were once openly hostile towards Jews, were ascendant. And so the indifference to the suffering of Jews exhibited by parts of the left, the uptick in antisemitic attac…

Percolation on random 2-lifts
Paul Drouvill\'e
https://arxiv.org/abs/2506.01612 https://arxiv.org/pdf/2506.01612

Percolation on random 2-lifts
Given a graph $G$, we consider a model for a random cover of $G$ by taking two parallel copies of $G$ and crossing every pair of parallel edges randomly with probability $q$ independently of each other. The resulting graph $G_q$, is a random $2$-lift of $G$ that may not be transitive but still probabilistically exhibit many properties of transitive graphs. Studying percolation in this context can help us test the reliability and robustness of our proofs methods in percolation theory. Our three …

From HOSTS.TXT to Modern Internet Infrastructure
🌐 #hoststxt

From HOSTS.TXT to Modern Internet Infrastructure | AXON Shield
The development of DNS demonstrates an impressive journey from its initial basic form into a modern distributed system which provides high resilience. The internet initially used a basic centralized text file named HOSTS.TXT for its operations. The rapid internet expansion made the initial text file system unworkable so developers created a new solution which could scale dynamically. The system evolves because organizations need better scalability and absolute reliability along with robust secu…

Flux Trapping Characterization for Superconducting Electronics Using a Cryogenic Widefield NV-Diamond Microscope
Rohan T. Kapur, Pauli Kehayias, Sergey K. Tolpygo, Adam A. Libson, George Haldeman, Collin N. Muniz, Alex Wynn, Nathaniel J. O'Connor, Neel A. Parmar, Ryan Johnson, Andrew C. Maccabe, John Cummings, Justin L. Mallek, Danielle A. Braje, Jennifer M. Schloss

Flux Trapping Characterization for Superconducting Electronics Using a Cryogenic Widefield NV-Diamond Microscope
Magnetic flux trapping is a significant hurdle limiting reliability and scalability of superconducting electronics, yet tools for imaging flux vortices remain slow or insensitive. We present a cryogenic widefield NV-diamond magnetic microscope capable of rapid, micron-scale imaging of flux trapping in superconducting devices. Using this technique, we measure vortex expulsion fields in Nb thin films and patterned strips, revealing a crossover in expulsion behavior between $10$ and $20~μ$m strip…

This https://arxiv.org/abs/2406.13945 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks
As large language models (LLMs) continue to advance and gain widespread use, establishing systematic and reliable evaluation methodologies for LLMs and vision-language models (VLMs) has become essential to ensure their real-world effectiveness and reliability. There have been some early explorations about the usability of LLMs for limited urban tasks, but a systematic and scalable evaluation benchmark is still lacking. The challenge in constructing a systematic evaluation benchmark for urban re…

This https://arxiv.org/abs/2502.19679 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDL_…

Architectural Vulnerability and Reliability Challenges in AI Text Annotation: A Survey-Inspired Framework with Independent Probability Assessment
Large Language Models, despite their power, have a fundamental architectural vulnerability stemming from their causal transformer design -- order sensitivity. This architectural constraint may distorts classification outcomes when prompt elements like label options are reordered, revealing a theoretical gap between accuracy metrics and true model reliability. The paper conceptualizes this vulnerability through the lens of survey methodology, where respondent biases parallel LLM positional depen…

Generative AI and Organizational Structure in the Knowledge Economy
Fasheng Xu, Jing Hou, Wei Chen, Karen Xie
https://arxiv.org/abs/2506.00532 https://

Generative AI and Organizational Structure in the Knowledge Economy
The adoption of GenAI is fundamentally reshaping organizations in the knowledge economy. GenAI can significantly enhance workers' problem-solving abilities and productivity, yet it also presents a major reliability challenge: hallucinations, or errors presented as plausible outputs. This study develops a theoretical model to examine GenAI's impact on organizational structure and the role of human-in-the-loop oversight. Our findings indicate that successful GenAI adoption hinges primarily on mai…

This https://arxiv.org/abs/2503.17770 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Probabilistic Net Load Forecasting for High-Penetration RES Grids Utilizing Enhanced Conditional Diffusion Model
The proliferation of intermittent distributed renewable energy sources (RES) in modern power systems has fundamentally compromised the reliability and accuracy of deterministic net load forecasting. Generative models, particularly diffusion models, demonstrate exceptional potential in uncertainty quantification for scenario forecasting. Nevertheless, their probabilistic predictive capabilities and conditional bootstrapping mechanisms still remain underexplored. In this paper, a day-ahead probab…

Accelerating Randomized Algorithms for Low-Rank Matrix Approximation
Dandan Jiang, Bo Fu, Weiwei Xu
https://arxiv.org/abs/2506.03859 https://

Accelerating Randomized Algorithms for Low-Rank Matrix Approximation
Randomized algorithms are overwhelming methods for low-rank approximation that can alleviate the computational expenditure with great reliability compared to deterministic algorithms. A crucial thought is generating a standard Gaussian matrix $\mathbf{G}$ and subsequently obtaining the orthonormal basis of the range of $\mathbf{AG}$ for a given matrix $\mathbf{A}$. Recently, the \texttt{farPCA} algorithm offers a framework for randomized algorithms, but the dense Gaussian matrix remains computa…

Delirium often goes underreported, making it hard for #AI to predict accurately. Our review of 120 studies found common data issues that hurt model reliability. We created a step-by-step guide to help build fairer, more accurate models—so we eventually can detect #delirium earlier and im…

Assessing Future Wind Energy Potential under Climate Change: The Critical Role of Multi-Model Ensembles in Robustness Assessment
Andrea Lira-Loarca, Francesco Ferrari, Andrea Mazzino
https://arxiv.org/abs/2505.24463

Assessing Future Wind Energy Potential under Climate Change: The Critical Role of Multi-Model Ensembles in Robustness Assessment
Accurate projections of wind energy potential under climate change are critical for effective long-term energy planning. While previous studies have highlighted the value of multi-model ensembles, they often fall short in capturing the full spectrum of uncertainties and temporal dynamics relevant to wind resource reliability. This paper presents one of the most comprehensive assessments to date, leveraging a large ensemble of 21 high-resolution RCM-GCM combinations from the EURO-CORDEX initiati…

This https://arxiv.org/abs/2502.15865 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Standard Benchmarks Fail -- Auditing LLM Agents in Finance Must Prioritize Risk
Standard benchmarks fixate on how well large language model (LLM) agents perform in finance, yet say little about whether they are safe to deploy. We argue that accuracy metrics and return-based scores provide an illusion of reliability, overlooking vulnerabilities such as hallucinated facts, stale data, and adversarial prompt manipulation. We take a firm position: financial LLM agents should be evaluated first and foremost on their risk profile, not on their point-estimate performance. Drawing…

NetPress: Dynamically Generated LLM Benchmarks for Network Applications
Yajie Zhou, Jiajun Ruan, Eric S. Wang, Sadjad Fouladi, Francis Y. Yan, Kevin Hsieh, Zaoxing Liu
https://arxiv.org/abs/2506.03231

NetPress: Dynamically Generated LLM Benchmarks for Network Applications
Despite growing interest in domain-specific benchmarking of large language models (LLMs) and agents, current evaluations remain limited to static, small-scale datasets, especially in high-stakes tasks like network operations that demand reliability for deployments. We present NetPress, an automated benchmark generation framework for evaluating LLM agents in network applications. NetPress introduces a unified abstraction with state and action, enabling dynamic generation of diverse query sets al…

This https://arxiv.org/abs/2502.02389 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Rate-reliability tradeoff for deterministic identification
We investigate deterministic identification over arbitrary memoryless channels under the constraint that the error probabilities of first and second kind are exponentially small in the block length $n$, controlled by reliability exponents $E_1,E_2 \geq 0$. In contrast to the regime of slowly vanishing errors, where the identifiable message length scales linearithmically as $Θ(n\log n)$, here we find that for positive exponents linear scaling is restored, now with a rate that is a function of t…

Automated Web Application Testing: End-to-End Test Case Generation with Large Language Models and Screen Transition Graphs
Nguyen-Khang Le, Quan Minh Bui, Minh Ngoc Nguyen, Hiep Nguyen, Trung Vo, Son T. Luu, Shoshin Nomura, Minh Le Nguyen
https://arxiv.org/abs/2506.02529

Automated Web Application Testing: End-to-End Test Case Generation with Large Language Models and Screen Transition Graphs
Web applications are critical to modern software ecosystems, yet ensuring their reliability remains challenging due to the complexity and dynamic nature of web interfaces. Recent advances in large language models (LLMs) have shown promise in automating complex tasks, but limitations persist in handling dynamic navigation flows and complex form interactions. This paper presents an automated system for generating test cases for two key aspects of web application testing: site navigation and form …

AI Data Development: A Scorecard for the System Card Framework
Tadesse K. Bahiru, Haileleol Tibebu, Ioannis A. Kakadiaris
https://arxiv.org/abs/2506.02071 …

AI Data Development: A Scorecard for the System Card Framework
Artificial intelligence has transformed numerous industries, from healthcare to finance, enhancing decision-making through automated systems. However, the reliability of these systems is mainly dependent on the quality of the underlying datasets, raising ongoing concerns about transparency, accountability, and potential biases. This paper introduces a scorecard designed to evaluate the development of AI datasets, focusing on five key areas from the system card framework data development life cy…

This https://arxiv.org/abs/2505.16214 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Behavioral Safety Assessment towards Large-scale Deployment of Autonomous Vehicles
Autonomous vehicles (AVs) have significantly advanced in real-world deployment in recent years, yet safety continues to be a critical barrier to widespread adoption. Traditional functional safety approaches, which primarily verify the reliability, robustness, and adequacy of AV hardware and software systems from a vehicle-centric perspective, do not sufficiently address the AV's broader interactions and behavioral impact on the surrounding traffic environment. To overcome this limitation, we pr…

This https://arxiv.org/abs/2404.00306 has been replaced.
link: https://scholar.google.com/scholar?q=a

A blockchain-based intelligent recommender system framework for enhancing supply chain resilience
This research proposed a data-driven supply chain disruption response baseline framework based on intelligent recommender system technology as an initial SCRes reactive solution. To improve the data quality and reliability of the proposed IRS as a stable, secure, and resilient decision support system, blockchain technology is integrated into the baseline architecture. The smart contract is prototyped to demonstrate the information exchange mechanism under a BLC network environment. The BLC-IRS …

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

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

Automated Formal Verification of Area-Optimized Safety Registers in Automotive SoCs
Shuhang Zhang, Bryan Olmos
https://arxiv.org/abs/2505.17990 https://

Automated Formal Verification of Area-Optimized Safety Registers in Automotive SoCs
Registers are primary storage elements in System-on-chip~(SoC) designs and play an important role in maintaining state information and processing data in digital systems. With respect to the ISO26262 standard, these registers require high levels of reliability and fault tolerance. For this reason, safety-critical applications require that normal registers are equipped with additional safety components to construct safety registers, which ensure system stability and fault tolerance. However, the…

An LLM Agent for Functional Bug Detection in Network Protocols
Mingwei Zheng, Chengpeng Wang, Xuwei Liu, Jinyao Guo, Shiwei Feng, Xiangyu Zhang
https://arxiv.org/abs/2506.00714

An LLM Agent for Functional Bug Detection in Network Protocols
Functional correctness is critical for ensuring the reliability and security of network protocol implementations. Functional bugs, instances where implementations diverge from behaviors specified in RFC documents, can lead to severe consequences, including faulty routing, authentication bypasses, and service disruptions. Detecting these bugs requires deep semantic analysis across specification documents and source code, a task beyond the capabilities of traditional static analysis tools. This p…

This https://arxiv.org/abs/2501.01965 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Linear inelastic kinetic equations modelling the spread of fake news and its interplay with personal awareness
In this paper, we introduce a kinetic model which describes a learning process leading individuals to build personal awareness about fake news. Next, we embed the results of this model into another kinetic model, which describes the popularity gained by news on social media conditioned to the reliability of the disseminated information. Both models are formulated in terms of linear inelastic Boltzmann-type equations, of which we investigate the main analytical properties - existence and uniquen…

Damping LFOs: Grid Following with Power Oscillation Damping vs. Grid Forming vs. PSS
Tamojit Chakraborty, Anamitra Pal, Sam Maleki
https://arxiv.org/abs/2505.24204

Damping LFOs: Grid Following with Power Oscillation Damping vs. Grid Forming vs. PSS
Low-frequency oscillations (LFOs) present a significant challenge to the stability and reliability of power systems, especially in grids with a high penetration of renewable energy sources. Traditional grid-following (GFL) inverters have proven less effective in damping such oscillations. This paper presents a GFL-power plant controller with an auxiliary power oscillation damping control for damping LFOs. This approach is compared with a traditional power system stabilizer (PSS) for a two-area …

Thanks to PostgreSQL for being a Gold Sponsor of SCALE 22x https://www.socallinuxexpo.org/scale/22x/sponsor/postgresql

Forcrat: Automatic I/O API Translation from C to Rust via Origin and Capability Analysis
Jaemin Hong, Sukyoung Ryu
https://arxiv.org/abs/2506.01427 https:/…

Forcrat: Automatic I/O API Translation from C to Rust via Origin and Capability Analysis
Translating C to Rust is a promising way to enhance the reliability of legacy system programs. Although the industry has developed an automatic C-to-Rust translator, C2Rust, its translation remains unsatisfactory. One major reason is that C2Rust retains C standard library (libc) function calls instead of replacing them with functions from the Rust standard library (Rust std). However, little work has been done on replacing library functions in C2Rust-generated code. In this work, we focus on re…

Integrating Expert Knowledge and Recursive Bayesian Inference: A Framework for Spatial and Spatio-Temporal Data Challenges
Mario Figueira, David Conesa, Antonio L\'opez-Qu\'ilez, H{\aa}vard Rue
https://arxiv.org/abs/2506.00221

Integrating Expert Knowledge and Recursive Bayesian Inference: A Framework for Spatial and Spatio-Temporal Data Challenges
Expert elicitation is a critical approach for addressing data scarcity across various disciplines. But moreover, it can also complement big data analytics by mitigating the limitations of observational data, such as incompleteness and reliability issues, thereby enhancing model estimates through the integration of disparate or conflicting data sources. The paper also outlines various strategies for integrating prior information within the Integrated Nested Laplace Approximation method and propo…

ProjMC$^2$: Scalable and Stable Posterior Inference for Bayesian Spatial Factor Models with Application to Spatial Transcriptomics
Lu Zhang
https://arxiv.org/abs/2506.01098

ProjMC$^2$: Scalable and Stable Posterior Inference for Bayesian Spatial Factor Models with Application to Spatial Transcriptomics
Factor models exhibit a fundamental tradeoff among flexibility, identifiability, and computational efficiency. Bayesian spatial factor models, in particular, face pronounced identifiability concerns and scaling difficulties. To mitigate these issues and enhance posterior inference reliability, this work proposes Projected Markov Chain Monte Carlo (ProjMC$^2$), a novel Markov Chain Monte Carlo (MCMC) sampling algorithm employing projection techniques and conditional conjugacy. ProjMC$^2$ is show…

This https://arxiv.org/abs/2505.16508 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Edge-First Language Model Inference: Models, Metrics, and Tradeoffs
The widespread adoption of Language Models (LMs) across industries is driving interest in deploying these services across the computing continuum, from the cloud to the network edge. This shift aims to reduce costs, lower latency, and improve reliability and privacy. Small Language Models (SLMs), enabled by advances in model compression, are central to this shift, offering a path to on-device inference on resource-constrained edge platforms. This work examines the interplay between edge and clo…

B2LoRa: Boosting LoRa Transmission for Satellite-IoT Systems with Blind Coherent Combining
Yimin Zhao, Weibo Wang, Xiong Wang, Linghe Kong, Jiadi Yu, Yifei Zhu, Shiyuan Li, Chong He, Guihai Chen
https://arxiv.org/abs/2505.24140

B2LoRa: Boosting LoRa Transmission for Satellite-IoT Systems with Blind Coherent Combining
With the rapid growth of Low Earth Orbit (LEO) satellite networks, satellite-IoT systems using the LoRa technique have been increasingly deployed to provide widespread Internet services to low-power and low-cost ground devices. However, the long transmission distance and adverse environments from IoT satellites to ground devices pose a huge challenge to link reliability, as evidenced by the measurement results based on our real-world setup. In this paper, we propose a blind coherent combining d…

Sensitivity of DC Network Representation for GIC Analysis
Aniruddh Mishra, Arthur K. Barnes, Jose E. Tabarez, Adam Mate
https://arxiv.org/abs/2505.23016 ht…

Sensitivity of DC Network Representation for GIC Analysis
Geomagnetic disturbances are a threat to the reliability and security of our national critical energy infrastructures. These events specifically result in geomagnetically induced currents, which can cause damage to transformers due to magnetic saturation. In order to mitigate these effects, blocker devices must be placed in optimal locations. Finding this placement requires a dc representation of the ac transmission lines, which this paper discusses. Different decisions in this process, includi…

The bias of IID resampled backtests for rolling-window mean-variance portfolios
Andrew Paskaramoorthy, Terence van Zyl, Tim Gebbie
https://arxiv.org/abs/2505.06383

The bias of IID resampled backtests for rolling-window mean-variance portfolios
Backtests on historical data are the basis for practical evaluations of portfolio selection rules, but their reliability is often limited by reliance on a single sample path. This can lead to high estimation variance. Resampling techniques offer a potential solution by increasing the effective sample size, but can disrupt the temporal ordering inherent in financial data and introduce significant bias. This paper investigates the critical questions: First, How large is this bias for Sharpe Ratio…

Context-Aware Semantic Communication for the Wireless Networks
Guangyuan Liu, Yinqiu Liu, Jiacheng Wang, Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, Abbas Jamalipour
https://arxiv.org/abs/2505.23249

Context-Aware Semantic Communication for the Wireless Networks
In next-generation wireless networks, supporting real-time applications such as augmented reality, autonomous driving, and immersive Metaverse services demands stringent constraints on bandwidth, latency, and reliability. Existing semantic communication (SemCom) approaches typically rely on static models, overlooking dynamic conditions and contextual cues vital for efficient transmission. To address these challenges, we propose CaSemCom, a context-aware SemCom framework that leverages a Large L…