February 2026

arXiv:2602.10129v1 Announce Type: new Abstract: Personalizing ad load in large-scale social networks requires balancing user experience and conversions under operational constraints. Traditional primal-dual methods enforce constraints reliably but adapt slowly in dynamic environments, while Bayesian Optimization (BO) enables exploration but suffers from slow convergence. We propose a hybrid online adaptive optimization framework CTRCBO ( Cohort-Based Trust Region Contextual Bayesian Optimization), combining primal-dual with BO, enhanced by trust-region updates and Gaussian Process Regression (GPR) surrogates for both objectives and […]

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arXiv:2602.10127v1 Announce Type: new Abstract: The rapid advancement of artificial intelligence (AI) agents has catalyzed the transition from static language models to autonomous agents capable of tool use, long-term planning, and social interaction. $textbf{Moltbook}$, the first social network designed exclusively for AI agents, has experienced viral growth in early 2026. To understand the behavior of AI agents in the agent-native community, in this paper, we present a large-scale empirical analysis of Moltbook leveraging a dataset of 44,411 posts […]

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arXiv:2602.10125v1 Announce Type: new Abstract: Network segmentation is a popular security practice for limiting lateral movement, yet practitioners lack a metric to measure how segmented a network actually is. We model a network as a graph and study segmentedness as a property captured by the global edge density that can be estimated from sampled node pairs. Then, we derive an estimator and evaluate its uncertainty using confidence intervals. For a 95% confidence interval with a margin-of-error of $pm […]

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arXiv:2602.10122v1 Announce Type: new Abstract: Agentic AI represents a significant shift in how intelligence is applied within organizations, moving beyond AI-assisted tools toward autonomous systems capable of reasoning, decision-making, and coordinated action across workflows. As these systems mature, they have the potential to automate a substantial share of manual organizational processes, fundamentally reshaping how work is designed, executed, and governed. Although many organizations have adopted AI to improve productivity, most implementations remain limited to isolated use cases and […]

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arXiv:2602.10119v1 Announce Type: new Abstract: Accurate prediction of functional outcomes after acute ischemic stroke can inform clinical decision-making and resource allocation. Prior work on modified Rankin Scale (mRS) prediction has relied primarily on structured variables (e.g., age, NIHSS) and conventional machine learning. The ability of large language models (LLMs) to infer future mRS scores directly from routine admission notes remains largely unexplored. We evaluated encoder (BERT, NYUTron) and generative (Llama-3.1-8B, MedGemma-4B) LLMs, in both frozen and fine-tuned settings, […]

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arXiv:2602.10118v1 Announce Type: new Abstract: Peer review is central to scientific quality, yet reliance on simple heuristics — lazy thinking — has lowered standards. Prior work treats lazy thinking detection as a single-label task, but review segments may exhibit multiple issues, including broader clarity problems, or specificity issues. Turning detection into actionable improvements requires guideline-aware feedback, which is currently missing. We introduce an LLM-driven framework that decomposes reviews into argumentative segments, identifies issues via a neurosymbolic module combining […]

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