Demand forecasting in supply chain management (SCM) is critical for optimizing inventory, reducing waste, and improving customer satisfaction. Conventional approaches frequently neglect external influences like weather, festivities, and equipment breakdowns, resulting in inefficiencies. This research investigates the use of machine learning (ML) algorithms to improve demand prediction in retail and vending machine sectors. Four machine learning algorithms. Extreme Gradient Boosting (XGBoost), Autoregressive Integrated Moving Average (ARIMA), Facebook Prophet (Fb Prophet), and Support Vector Regression (SVR) were used to […]

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The digital marketing is like a forest fire, with generative AI becoming viral in other industry segments as well. It is a financial revolution. The potential of the generative AI is effective and correct and can save a large amount of time not only to accelerate the end of the month closing but also detail the financial stories. But all their novelty and convenience cannot be buried under the carpet of any cobbles of ethical problems. The Temptation […]

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arXiv:2601.18145v1 Announce Type: new Abstract: Computation of confidence sets is central to data science and machine learning, serving as the workhorse of A/B testing and underpinning the operation and analysis of reinforcement learning algorithms. Among all valid confidence sets for the multinomial parameter, minimum-volume confidence sets (MVCs) are optimal in that they minimize average volume, but they are defined as level sets of an exact p-value that is discontinuous and difficult to compute. Rather than attempting to characterize […]

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arXiv:2603.03324v1 Announce Type: new Abstract: Controlling stylistic attributes in large language models (LLMs) remains challenging, with existing approaches relying on either prompt engineering or post-training alignment. This paper investigates this challenge through the lens of representation engineering, testing the hypothesis that distinct stylistic attributes – from emotional tone to linguistic structure – are encoded as linear directions in the model’s activation space. We provide strong empirical evidence for this hypothesis across a wide range of styles and, based […]

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Deep learning, despite its remarkable successes, is a young field – perhaps ten years old. While models called artificial neural networks have been studied for decades, much of that work seems only tenuously connected to modern results. It’s often the case that young fields start in a very ad-hoc manner. Later, the mature field is understood very differently than it was understood by its early practitioners. It seems quite likely that deep learning is in this ad-hoc state… […]

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arXiv:2601.15429v1 Announce Type: new Abstract: Large Language Models (LLMs) generate fluent answers but can struggle with trustworthy, domain-specific reasoning. We evaluate whether domain knowledge graphs (KGs) improve Retrieval-Augmented Generation (RAG) for healthcare by constructing three PubMed-derived graphs: $mathbb{G}_1$ (T2DM), $mathbb{G}_2$ (Alzheimer’s disease), and $mathbb{G}_3$ (AD+T2DM). We design two probes: Probe 1 targets merged AD T2DM knowledge, while Probe 2 targets the intersection of $mathbb{G}_1$ and $mathbb{G}_2$. Seven instruction-tuned LLMs are tested across retrieval sources {No-RAG, $mathbb{G}_1$, $mathbb{G}_2$, $mathbb{G}_1$ […]

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arXiv:2602.18514v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly integrated into automated decision-making pipelines, specifically within Human Resources (HR), the security implications of Indirect Prompt Injection (IPI) become critical. While a prevailing hypothesis posits that “Reasoning” or “Chain-of-Thought” Models possess safety advantages due to their ability to self-correct, emerging research suggests these capabilities may enable more sophisticated alignment failures. This qualitative Red-Teaming case study challenges the safety-through-reasoning premise using the Qwen 3 30B architecture. By […]

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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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