As companies grow, they can expand their offerings, reach new customers and markets, enhance profitability, and position themselves ahead of competitors. Whether trying to expand their company size by adding new departments or increase revenue by improving productivity per employee, companies can achieve growth through various strategies. One of the strongest company growth strategies is retaining skilled employees who drive productivity, innovation, morale, and customer satisfaction. Employee retention is a company’s ability to maintain a stable workforce by […]

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arXiv:2602.21337v1 Announce Type: new Abstract: AI is becoming increasingly integrated into everyday life, both in professional work environments and in leisure and entertainment contexts. This integration requires AI to move beyond acting as an assistant for informational or transactional tasks toward a genuine collaborative partner. Effective collaboration, whether between humans or between humans and AI, depends on establishing and maintaining common ground: shared beliefs, assumptions, goals, and situational awareness that enable coordinated action and efficient repair of misunderstandings. […]

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arXiv:2603.06626v1 Announce Type: new Abstract: Traditional Mixture-of-Experts (MoE) training typically proceeds without any structural priors, effectively requiring the model to simultaneously train expert weights while searching for an optimal routing policy within a vast combinatorial space. This entanglement often leads to sluggish convergence and training instabilities. This paper introduces Grouter, a preemptive routing method that by distilling high-quality structures from fully-trained MoE models and serving as a fixed router for target models. By decoupling structural optimization from weight […]

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Progress in hardware model checking depends critically on high-quality benchmarks. However, the community faces a significant benchmark gap: existing suites are limited in number, often distributed only in representations such as BTOR2 without access to the originating register-transfer-level (RTL) designs, and biased toward extreme difficulty where instances are either trivial or intractable. These limitations hinder rigorous evaluation of new verification techniques and encourage overfitting of solver heuristics to a narrow set of problems. To address this, we introduce […]

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A basic unanswered question in neural network training is: what is the best learning rate schedule shape for a given workload? The choice of learning rate schedule is a key factor in the success or failure of the training process, but beyond having some kind of warmup and decay, there is no consensus on what makes a good schedule shape. To answer this question, we designed a search procedure to find the best shapes within a parameterized schedule […]

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This article covers how Azure ML’s persistent, workspace-centric compute resources differ from AWS SageMaker’s on-demand, job-specific approach. Additionally, we explored environment customization options, from Azure’s curated environments and custom environments to SageMaker’s three level of customizations. The post AWS vs. Azure: A Deep Dive into Model Training – Part 2 appeared first on Towards Data Science.

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In late September, the United Kingdom’s Prime Minister Keir Starmer announced his government’s plans to introduce a new digital ID scheme in the country to take effect before the end of the Parliament (no later than August 2029). The scheme will, according to the Prime Minister, “cut the faff” in proving people’s identities by creating a virtual ID on personal devices with information like people’s name, date of birth, nationality or residency status, and photo to verify their […]

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