If one looks attentively, without superfluous emotions, at the technological market of the beginning of 2026, one can sense a strange, I would even say, paradoxical cognitive dissonance. The situation is twofold. On the one hand, we observe incredible, simply cosmic technical progress: models have become smarter, they have become faster and, what is critically important thanks to aggressive price pressure from the side of new ambitious players like DeepSeek, they have become dramatically cheaper. Computational capacities, which […]

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arXiv:2603.23560v1 Announce Type: new Abstract: We develop the first algorithms for computing the Skyscraper Invariant [FJNT24]. This is a filtration of the classical rank invariant for multiparameter persistence modules defined by the Harder-Narasimhan filtrations along every central charge supported at a single parameter value. Cheng’s algorithm [Cheng24] can be used to compute HN filtrations of arbitrary acyclic quiver representations in polynomial time in the total dimension, but in practice, the large dimension of persistence modules makes this direct […]

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arXiv:2601.08999v1 Announce Type: new Abstract: Accurate prediction of solar energetic particle events is vital for safeguarding satellites, astronauts, and space-based infrastructure. Modern space weather monitoring generates massive volumes of high-frequency, multivariate time series (MVTS) data from sources such as the Geostationary perational Environmental Satellites (GOES). Machine learning (ML) models trained on this data show strong predictive power, but most existing methods overlook domain-specific feasibility constraints. Counterfactual explanations have emerged as a key tool for improving model interpretability, yet […]

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For the past few years, I have spent time trying to bend machine learning into something useful for Amazon FBA demand forecasting. On paper, it did sound straightforward to me with all these past sales in and future demand out. But in reality, Amazon’s data is messy, has so many missing pieces, and changes all the time, which makes it hard to build a reliable model. If you have ever wondered why your Prophet model looked great in […]

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arXiv:2603.20211v1 Announce Type: new Abstract: AI-based chatbots have the potential to accelerate learning and teaching, but may also have counterproductive consequences without thoughtful design and scaffolding. To better understand teachers’ perspectives on large language model (LLM)-based chatbots, we conducted a study with 11 teams of middle school teachers using chatbots for a science and computational thinking activity within a block-based programming environment. Based on a qualitative analysis of audio transcripts and chatbot interactions, we propose three profiles: explorer, […]

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arXiv:2602.21331v1 Announce Type: new Abstract: General-purpose simulators have accelerated the development of robots. Traditional simulators based on first-principles, however, typically require full-state observability or depend on parameter search for system identification. This work presents texttt{CableRobotGraphSim}, a novel Graph Neural Network (GNN) model for cable-driven robots that aims to address shortcomings of prior simulation solutions. By representing cable-driven robots as graphs, with the rigid-bodies as nodes and the cables and contacts as edges, this model can quickly and accurately […]

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The two tech giants are looking to co-develop custom chips, at a time when demand for CPUs is high due to a growing global shortage.

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arXiv:2601.08012v1 Announce Type: new Abstract: Large language model (LLM)-based AI agents extend LLM capabilities by enabling access to tools such as data sources, APIs, search engines, code sandboxes, and even other agents. While this empowers agents to perform complex tasks, LLMs may invoke unintended tool interactions and introduce risks, such as leaking sensitive data or overwriting critical records, which are unacceptable in enterprise contexts. Current approaches to mitigate these risks, such as model-based safeguards, enhance agents’ reliability but […]

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arXiv:2603.12304v1 Announce Type: new Abstract: This paper introduces a novel optimization framework that fundamentally integrates the Minimum Description Length (MDL) principle into the training dynamics of deep neural networks. Moving beyond its conventional role as a model selection criterion, we reformulate MDL as an active, adaptive driving force within the optimization process itself. The core of our method is a geometrically-grounded cognitive manifold whose evolution is governed by a textit{coupled Ricci flow}, enriched with a novel textit{MDL Drive} […]

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