arXiv:2602.04872v1 Announce Type: new Abstract: Recent progress has rapidly advanced our understanding of the mechanisms underlying in-context learning in modern attention-based neural networks. However, existing results focus exclusively on unimodal data; in contrast, the theoretical underpinnings of in-context learning for multi-modal data remain poorly understood. We introduce a mathematically tractable framework for studying multi-modal learning and explore when transformer-like architectures can recover Bayes-optimal performance in-context. To model multi-modal problems, we assume the observed data arises from a latent […]

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Two-photon lithography (TPL) is an advanced additive manufacturing (AM) technique for fabricating high-precision micro-structures. While computer vision (CV) is proofed for automated quality control, existing models are often static, rendering them ineffective in dynamic manufacturing environments. These models typically cannot detect new, unseen defect classes, be efficiently updated from scarce data, or adapt to new part geometries. To address this gap, this paper presents an adaptive CV framework for the entire life-cycle of quality model maintenance. The proposed […]

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Victoria, Seychelles, January 29, 2026 MEXC, the world’s fastest-growing digital asset exchange and a pioneer of true zero-fee trading, launched a limited-time MEXC Earn event to celebrate the listing of USAT, providing users with the opportunity to share 300,000 USAT and earn up to 300% APR. USAT is Tether’s first US-regulated stablecoin, designed to comply with the GENIUS Act, which was signed into law in July 2025. Each USAT token maintains a 1:1 peg with the US dollar, […]

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For many businesses, data science drives strategic decisions; as a result, most initiatives to improve analytics lack measurable impact. Dashboards are created, models are deployed, forecasts produced but leadership still poses an old question: Did this move the needle? However, tackling this question, beyond predicting, toward measurement of causally based on data and insights, is something econometric data science can provide a solid foundation for. The Measurement Problem in Business Analytics The vast majority of the metrics in […]

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The Job-Shop Scheduling Problem (JSSP), under various forms of manufacturing uncertainty, has recently attracted considerable research attention. Most existing studies focus on parameter uncertainty, such as variable processing times, and typically adopt the actor-critic framework. In this paper, we explore a different but prevalent form of uncertainty in JSSP: structural uncertainty. Structural uncertainty arises when a job may follow one of several routing paths, and the selection is determined not by policy, but by situational factors (e.g., the […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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An intuitive, step-by-step look at how Transformers use self-attention to turn static word embeddings into contextual representations, illustrated with simple examples and an Excel-friendly walkthrough. The post The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel appeared first on Towards Data Science.

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