arXiv:2603.02291v1 Announce Type: new Abstract: We investigate an integrated sensing and communication (ISAC)-enabled BS for the unmanned aerial vehicle (UAV) obstacle avoidance task, and propose a goal-oriented semantic communication (GOSC) framework for the BS to transmit sensing and command and control (C&C) signals efficiently and effectively. Our GOSC framework establishes a closed loop for sensing-C&C generation-sensing and C&C transmission: For sensing, a Kalman filter (KF) is applied to continuously predict UAV positions, mitigating the reliance of UAV position […]

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arXiv:2601.04359v1 Announce Type: new Abstract: A unified autoregressive model is a Transformer-based framework that addresses diverse multimodal tasks (e.g., text, image, video) as a single sequence modeling problem under a shared token space. Such models rely on the KV-cache mechanism to reduce attention computation from O(T^2) to O(T); however, KV-cache size grows linearly with the number of generated tokens, and it rapidly becomes the dominant bottleneck limiting inference efficiency and generative length. Unified autoregressive video generation inherits this […]

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Abu Dhabi, UAE – The Global AI Show Abu Dhabi 2025, which was held on December 8-9th, 2025, at the Space42 Arena in Abu Dhabi and organized by the VAP Group and powered by the Times of AI, has ended and continues to elevate Abu Dhabi’s reputation as a center for innovation, policy leadership, and corporate adoption of artificial intelligence. The 2-day exhibition welcomed 5,000+ attendees, 100+ speakers from all around the world, and a powerful group of […]

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Growth hacks work, until they don’t. Most startups begin with quick wins: viral reels, aggressive ads, referral incentives, clever copy. For a while, momentum builds. Metrics go up. Dashboards look healthy. It feels like progress. Then something changes. Your customer acquisition costs start rising. Engagement flattens. What once felt like growth starts to feel like a constant fight. At this point, you usually assume the answer is another tactic. A new channel. A new funnel. A new trick. […]

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arXiv:2602.23369v1 Announce Type: new Abstract: Traditional multimodal retrieval systems rely primarily on bi-encoder architectures, where performance is closely tied to embedding dimensionality. Recent work, Think-Then-Embed (TTE), shows that incorporating multimodal reasoning to elicit additional informative tokens before embedding can further improve retrieval. In this paper, we extend this paradigm with TTE-v2, a hybrid multimodal retrieval framework that introduces reasoning-driven performance scaling based on additional input token budget rather than model or embedding size. Our approach augments the initial […]

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arXiv:2601.10623v1 Announce Type: cross Abstract: We propose a unified framework for fair regression tasks formulated as risk minimization problems subject to a demographic parity constraint. Unlike many existing approaches that are limited to specific loss functions or rely on challenging non-convex optimization, our framework is applicable to a broad spectrum of regression tasks. Examples include linear regression with squared loss, binary classification with cross-entropy loss, quantile regression with pinball loss, and robust regression with Huber loss. We derive […]

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Organizations that have become more integrated into data science are changing from analytics to intelligence. Economic intelligence does not consist of building more models or dashboards; it is embedding a structured way of reasoning into decision systems. An econometrics-first approach provides a strong roadmap for designing those systems and to ensure that data-driven insights are communicated to action that is sound, and coherent. From Analytics Outputs to Decision Systems Conventional analytics pipelines usually have outputs of predictions or […]

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arXiv:2603.27270v1 Announce Type: cross Abstract: Credal sets, i.e., closed convex sets of probability measures, provide a natural framework to represent aleatoric and epistemic uncertainty in machine learning. Yet how to quantify these two types of uncertainty for a given credal set, particularly in multiclass classification, remains underexplored. In this paper, we propose a distance-based approach to quantify total, aleatoric, and epistemic uncertainty for credal sets. Concretely, we introduce a family of such measures within the framework of Integral […]

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