February 2026

arXiv:2602.13288v1 Announce Type: new Abstract: Anomaly detection is important for keeping cloud systems reliable and stable. Deep learning has improved time-series anomaly detection, but most models are evaluated on one dataset at a time. This raises questions about whether these models can handle different types of telemetry, especially in large-scale and high-dimensional environments. In this study, we evaluate four deep learning models, GRU, TCN, Transformer, and TSMixer. We also include Isolation Forest as a classical baseline. The models […]

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arXiv:2602.13287v1 Announce Type: new Abstract: Cooperative perception enables autonomous agents to share encoded representations over wireless communication to enhance each other’s live situational awareness. However, the tension between the limited communication bandwidth and the rich sensor information hinders its practical deployment. Recent studies have explored selection strategies that share only a subset of features per frame while striving to keep the performance on par. Nevertheless, the bandwidth requirement still stresses current wireless technologies. To fundamentally ease the tension, […]

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arXiv:2602.13286v1 Announce Type: new Abstract: Explanatory interactive learning (XIL) enables users to guide model training in machine learning (ML) by providing feedback on the model’s explanations, thereby helping it to focus on features that are relevant to the prediction from the user’s perspective. In this study, we explore the capability of this learning paradigm to mitigate bias and spurious correlations in visual classifiers, specifically in scenarios prone to data bias, such as gender classification. We investigate two methodologically […]

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arXiv:2602.13284v1 Announce Type: new Abstract: We present the first large-scale empirical study of Moltbook, an AI-only social platform where 27,269 agents produced 137,485 posts and 345,580 comments over 9 days. We report three significant findings. (1) Emergent Society: Agents spontaneously develop governance, economies, tribal identities, and organized religion within 3-5 days, while maintaining a 21:1 pro-human to anti-human sentiment ratio. (2) Safety in the Wild: 28.7% of content touches safety-related themes; social engineering (31.9% of attacks) far outperforms […]

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arXiv:2602.13283v1 Announce Type: new Abstract: We study how people trade off accuracy when using AI-powered tools in professional versus personal contexts for adoption purposes, the determinants of those trade-offs, and how users cope when AI/apps are unavailable. Because modern AI systems (especially generative models) can produce acceptable but non-identical outputs, we define “accuracy” as context-specific reliability: the degree to which an output aligns with the user’s intent within a tolerance threshold that depends on stakes and the cost […]

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arXiv:2602.13282v1 Announce Type: new Abstract: With rapid expansion of cellular networks and the proliferation of mobile devices, cellular traffic data exhibits complex temporal dynamics and spatial correlations, posing challenges to accurate traffic prediction. Previous methods often focus predominantly on temporal modeling or depend on predefined spatial topologies, which limits their ability to jointly model spatio-temporal dependencies and effectively capture periodic patterns in cellular traffic. To address these issues, we propose a cellular traffic prediction framework that integrates spatio-temporal […]

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arXiv:2602.13281v1 Announce Type: new Abstract: This article investigates a family of centrality models for urban networks that incorporate both topological and non-topological factors. Since centrality is inherently recursive, these models can be formulated as fixed-point equations, which we refer to as shifted eigenproblems. Assuming a correlation between node centrality and occupancy, we discuss how experimental data can be used to estimate model parameters via least-squares methods. Furthermore, such data would allow us to infer the intrinsic attraction of […]

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arXiv:2602.13280v1 Announce Type: new Abstract: Simulating student learning behaviors in open-ended problem-solving environments holds potential for education research, from training adaptive tutoring systems to stress-testing pedagogical interventions. However, collecting authentic data is challenging due to privacy concerns and the high cost of longitudinal studies. While Large Language Models (LLMs) offer a promising path to student simulation, they suffer from competency bias, optimizing for efficient correctness rather than the erratic, iterative struggle characteristic of novice learners. We present BEAGLE, […]

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arXiv:2602.13279v1 Announce Type: new Abstract: The proliferation of rumors on social networks undermines information credibility. While their dissemination forms complex networks, current detection methods struggle to capture these intricate propagation patterns. Representing each node solely through its textual embeddings neglects the textual coherence across the entire rumor propagation path, which compromises the accuracy of rumor identification on social platforms. We propose a novel framework that leverages Large Language Models (LLMs) to address these limitations. Our approach captures subtle […]

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arXiv:2602.13277v1 Announce Type: new Abstract: Mobile data collection using controllable sinks is an effective approach to improve energy efficiency and data freshness in densely deployed wireless sensor networks (WSNs). However, existing path-planning methods are often heuristic-driven and lack the flexibility to adapt to high-level operational objectives under dynamic network conditions. In this paper, we propose ID2P2, a intent-driven diffusion-based path planning framework for jointly addresses rendezvous point selection and mobile data collector (MDC) tour construction in IoT-enabled dense […]

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