Tootfinder

Network Traffic as a Scalable Ethnographic Lens for Understanding University Students' AI Tool Practices
Donghan Hu, Rameen Mahmood, Annabelle David, Danny Yuxing Huang
https://arxiv.org/abs/2510.09763

Network Traffic as a Scalable Ethnographic Lens for Understanding University Students' AI Tool Practices
AI-driven applications have become woven into students' academic and creative workflows, influencing how they learn, write, and produce ideas. Gaining a nuanced understanding of these usage patterns is essential, yet conventional survey and interview methods remain limited by recall bias, self-presentation effects, and the underreporting of habitual behaviors. While ethnographic methods offer richer contextual insights, they often face challenges of scale and reproducibility. To bridge this gap…

Structured Cooperative Multi-Agent Reinforcement Learning: a Bayesian Network Perspective
Shahbaz P Qadri Syed, He Bai
https://arxiv.org/abs/2510.09937 https://

Structured Cooperative Multi-Agent Reinforcement Learning: a Bayesian Network Perspective
The empirical success of multi-agent reinforcement learning (MARL) has motivated the search for more efficient and scalable algorithms for large scale multi-agent systems. However, existing state-of-the-art algorithms do not fully exploit inter-agent coupling information to develop MARL algorithms. In this paper, we propose a systematic approach to leverage structures in the inter-agent couplings for efficient model-free reinforcement learning. We model the cooperative MARL problem via a Bayesi…

Expressive Value Learning for Scalable Offline Reinforcement Learning
Nicolas Espinosa-Dice, Kiante Brantley, Wen Sun
https://arxiv.org/abs/2510.08218 https://

Expressive Value Learning for Scalable Offline Reinforcement Learning
Reinforcement learning (RL) is a powerful paradigm for learning to make sequences of decisions. However, RL has yet to be fully leveraged in robotics, principally due to its lack of scalability. Offline RL offers a promising avenue by training agents on large, diverse datasets, avoiding the costly real-world interactions of online RL. Scaling offline RL to increasingly complex datasets requires expressive generative models such as diffusion and flow matching. However, existing methods typically…

GrASP: A Generalizable Address-based Semantic Prefetcher for Scalable Transactional and Analytical Workloads
Farzaneh Zirak, Farhana Choudhury, Renata Borovica-Gajic
https://arxiv.org/abs/2510.11011

GrASP: A Generalizable Address-based Semantic Prefetcher for Scalable Transactional and Analytical Workloads
Data prefetching--loading data into the cache before it is requested--is essential for reducing I/O overhead and improving database performance. While traditional prefetchers focus on sequential patterns, recent learning-based approaches, especially those leveraging data semantics, achieve higher accuracy for complex access patterns. However, these methods often struggle with today's dynamic, ever-growing datasets and require frequent, timely fine-tuning. Privacy constraints may also restrict a…

Operationalizing AI: Empirical Evidence on MLOps Practices, User Satisfaction, and Organizational Context
Stefan Pasch
https://arxiv.org/abs/2510.09968 https://

Operationalizing AI: Empirical Evidence on MLOps Practices, User Satisfaction, and Organizational Context
Organizational efforts to utilize and operationalize artificial intelligence (AI) are often accompanied by substantial challenges, including scalability, maintenance, and coordination across teams. In response, the concept of Machine Learning Operations (MLOps) has emerged as a set of best practices that integrate software engineering principles with the unique demands of managing the ML lifecycle. Yet, empirical evidence on whether and how these practices support users in developing and operat…

PIKA: Expert-Level Synthetic Datasets for Post-Training Alignment from Scratch
Shangjian Yin, Shining Liang, Wenbiao Ding, Yuli Qian, Zhouxing Shi, Hongzhi Li, Yutao Xie
https://arxiv.org/abs/2510.06670

PIKA: Expert-Level Synthetic Datasets for Post-Training Alignment from Scratch
Reinforcement Learning from Human Feedback (RLHF) has become a cornerstone for aligning large language models (LLMs). However, its effectiveness depends on high-quality instruction data. Most existing alignment datasets are either private or require costly human annotation, which limits reproducibility and scalability. Even with Reinforcement Learning from AI Feedback (RLAIF), concerns about data quality remain. Moreover, it is unclear how much data is actually required to fine-tune a base mode…

REACH: Reinforcement Learning for Adaptive Microservice Rescheduling in the Cloud-Edge Continuum
Xu Bai, Muhammed Tawfiqul Islam, Rajkumar Buyya, Adel N. Toosi
https://arxiv.org/abs/2510.06675

REACH: Reinforcement Learning for Adaptive Microservice Rescheduling in the Cloud-Edge Continuum
Cloud computing, despite its advantages in scalability, may not always fully satisfy the low-latency demands of emerging latency-sensitive pervasive applications. The cloud-edge continuum addresses this by integrating the responsiveness of edge resources with cloud scalability. Microservice Architecture (MSA) characterized by modular, loosely coupled services, aligns effectively with this continuum. However, the heterogeneous and dynamic computing resource poses significant challenges to the op…

Accelerated Aggregated D-Optimal Designs for Estimating Main Effects in Black-Box Models
Chih-Yu Chang, Ming-Chung Chang
https://arxiv.org/abs/2510.08465 https://

Accelerated Aggregated D-Optimal Designs for Estimating Main Effects in Black-Box Models
Recent advances in supervised learning have driven growing interest in explaining black-box models, particularly by estimating the effects of input variables on model predictions. However, existing approaches often face key limitations, including poor scalability, sensitivity to out-of-distribution sampling, and instability under correlated features. To address these issues, we propose A2D2E, an $\textbf{E}$stimator based on $\textbf{A}$ccelerated $\textbf{A}$ggregated $\textbf{D}$-Optimal $\te…

Reliable and Scalable Robot Policy Evaluation with Imperfect Simulators
Apurva Badithela, David Snyder, Lihan Zha, Joseph Mikhail, Matthew O'Kelly, Anushri Dixit, Anirudha Majumdar
https://arxiv.org/abs/2510.04354

Reliable and Scalable Robot Policy Evaluation with Imperfect Simulators
Rapid progress in imitation learning, foundation models, and large-scale datasets has led to robot manipulation policies that generalize to a wide-range of tasks and environments. However, rigorous evaluation of these policies remains a challenge. Typically in practice, robot policies are often evaluated on a small number of hardware trials without any statistical assurances. We present SureSim, a framework to augment large-scale simulation with relatively small-scale real-world testing to prov…

CLAd-VR: Cognitive Load-based Adaptive Training for Machining Tasks in Virtual Reality
Bhavya Matam, Adamay Mann, Kachina Studer, Christian Gabbianelli, Sonia Castelo, John Liu, Claudio Silva, Dishita Turakhia
https://arxiv.org/abs/2510.05249

CLAd-VR: Cognitive Load-based Adaptive Training for Machining Tasks in Virtual Reality
With the growing need to effectively support workforce upskilling in the manufacturing sector, virtual reality is gaining popularity as a scalable training solution. However, most current systems are designed as static, step-by-step tutorials and do not adapt to a learner's needs or cognitive load, which is a critical factor in learning and longterm retention. We address this limitation with CLAd-VR, an adaptive VR training system that integrates realtime EEG-based sensing to measure the learne…