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arXiv:2601.17606v1 Announce Type: new Abstract: Performant all-to-all collective operations in MPI are critical to fast Fourier transforms, transposition, and machine learning applications. There are many...
arXiv:2601.17607v1 Announce Type: new Abstract: Learning systems acquire structured internal representations from data, yet classical information-theoretic results state that deterministic transformations do not increase information....
arXiv:2601.17608v1 Announce Type: new Abstract: To support aging-in-place, adult children often provide care to their aging parents from a distance. These informal caregivers desire plug-and-play...
arXiv:2601.17609v1 Announce Type: new Abstract: In domains like medicine and finance, large-scale labeled data is costly and often unavailable, leading to models trained on small...
arXiv:2601.17614v1 Announce Type: new Abstract: Designing user interfaces that align with user preferences is a time-consuming process, which requires iterative cycles of prototyping, user testing,...
arXiv:2601.17615v1 Announce Type: new Abstract: Prefetching and off-chip prediction are two techniques proposed to hide long memory access latencies in high-performance processors. In this work,...
arXiv:2601.17616v1 Announce Type: new Abstract: Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to...
arXiv:2601.17617v1 Announce Type: new Abstract: LLM-powered search agents are increasingly being used for multi-step information seeking tasks, yet the IR community lacks empirical understanding of...
arXiv:2601.17620v1 Announce Type: new Abstract: Biometric data is considered to be very private and highly sensitive. As such, many methods for biometric template protection were...
arXiv:2601.17622v2 Announce Type: new Abstract: We introduce Memento, a conversational AR assistant that permanently captures and memorizes user's verbal queries alongside their spatiotemporal and activity...
arXiv:2601.17625v1 Announce Type: new Abstract: Transformer-based neural decoders with large parameter counts, pre-trained on large-scale datasets, have recently outperformed classical machine learning models and small...
arXiv:2601.17627v1 Announce Type: new Abstract: AI coding agents can autonomously generate pull requests (PRs), yet little is known about how their contributions compare to those...
arXiv:2601.17631v1 Announce Type: new Abstract: The high-density, decentralized copyright conflicts triggered by generative AI training require more than ad hoc solutions; they demand structural governance...
arXiv:2601.17632v1 Announce Type: new Abstract: In cell-free massive multiple-input multiple-output (MIMO) networks, robust resource allocation is critical to ensure reliable system performance in the presence...
arXiv:2601.17633v1 Announce Type: new Abstract: Solid-state drives (SSDs) are well suited for near-data processing (NDP) because they: (1) store large application datasets, and (2) support...
arXiv:2601.17637v1 Announce Type: new Abstract: Autonomous systems increasingly require moral judgment capabilities, yet whether these capabilities scale predictably with model size remains unexplored. We systematically...
arXiv:2601.17638v1 Announce Type: new Abstract: In this work, we introduce FOCA, a novel multimodal framework for malware classification that jointly leverages audio and visual modalities....
arXiv:2601.17641v1 Announce Type: new Abstract: Brain decoding aims to interpret and translate neural activity into behaviors. As such, it is imperative that decoding models are...
arXiv:2601.17642v1 Announce Type: new Abstract: Safety alignment in Large Language Models is critical for healthcare; however, reliance on binary refusal boundaries often results in \emph{over-refusal}...
arXiv:2601.17644v2 Announce Type: new Abstract: The growing adoption of multimodal Retrieval-Augmented Generation (mRAG) pipelines for vision-centric tasks (e.g. visual QA) introduces important privacy challenges. In...
arXiv:2601.17646v1 Announce Type: new Abstract: Empirical risk minimization (ERM) stability is usually studied via single-valued outputs, while convex non-strict losses yield set-valued minimizers. We identify...
arXiv:2601.17647v1 Announce Type: new Abstract: Quantifying the causal relationship between ice melt and freshwater distribution is critical, as these complex interactions manifest as regional fluctuations...
arXiv:2601.17654v1 Announce Type: new Abstract: The computing demand of AI is growing at an unprecedented rate, but energy supply is not keeping pace. As a...
arXiv:2601.17656v1 Announce Type: new Abstract: This paper presents a battery-free and gateway-free water leak detection system capable of direct communication over LTE-M (Cat-M1). The system...