arXiv:2602.16716v1 Announce Type: new Abstract: Adaptive systems often operate across multiple contexts while reusing a fixed internal state space due to constraints on memory, representation, or physical resources. Such single-state reuse is ubiquitous in natural and artificial intelligence, yet its fundamental representational consequences remain poorly understood. We show that contextuality is not a peculiarity of quantum mechanics, but an inevitable consequence of single-state reuse in classical probabilistic representations. Modeling contexts as interventions acting on a shared internal state, […]

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Piotr Niedźwiedź, CEO/CTO and founder of neptune.ai I’m excited to share that we’ve entered into a definitive agreement to be acquired by OpenAI, subject to closing conditions. We are thrilled to join the OpenAI team and help their AI researchers build better models faster. We started in 2017, when the pivotal transformer paper came out. And from then until today, we have focused on helping people who build ML models during the iterative, messy, and unpredictable phase of […]

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arXiv:2602.15086v1 Announce Type: new Abstract: The numerical values extracted from a graph that indicates its topology are called topological indices. A contemporary and efficient method is to compute a graph’s topological indices using the graph polynomial that corresponds to it. This method of identifying degree-based topological indices involves the use of the M-polynomial. Very recently, in 2025, the hyperbolic Sombor index (HSO) was proposed and shows its chemical applicability for octane isomers and the structure sensitivity and abruptness […]

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Mixture density networks are neural networks that produce Gaussian mixtures to represent continuous multimodal conditional densities. Standard training procedures involve maximum likelihood estimation using the negative log-likelihood (NLL) objective, which suffers from slow convergence and mode collapse. In this work, we improve the optimization of mixture density networks by integrating their information geometry. Specifically, we interpret mixture density networks as deep latent-variable models and analyze them through an expectation maximization framework, which reveals surprising theoretical connections to natural […]

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Free-to-play games have become one of the dominant economic models in the digital games industry, especially in mobile-first markets. Although access to these games is free, revenue is generated through digital items, premium currencies, randomized reward systems, seasonal passes, and other monetization mechanisms. Existing studies have examined many of these features separately, but there is still a lack of a design-oriented framework that explains how player-perceived value is systematically converted into monetization. This paper proposes a Design Science […]

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Goldbach’s conjecture, one of the oldest and most resilient problems in number theory, has traditionally been approached through additive and combinatorial methods. Despite extensive numerical verification and partial results, a structural explanation for its apparent universality remains elusive. In this work, we propose a reinterpretation of Goldbach’s conjecture within the framework of Viscous Time Theory (VTT), introducing an informational–geometric perspective in which prime numbers are treated as stable coherence attractors in an informational field. Within this framework, the […]

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arXiv:2602.09181v1 Announce Type: new Abstract: Exploiting the analogy between Gaussian Distributions and Gaussian Processes’ posterior, we present how the weighted Wasserstein Barycenter of Gaussian Processes (W2BGP) can be used to unify, under a common framework, different exotic Bayesian Optimization (BO) tasks. Specifically, collaborative/federated BO, (synchronous) batch BO, and multi-fidelity BO are considered in this paper. Our empirical analysis proves that each one of these tasks requires just an appropriate weighting schema for the W2BGP, while the entire framework […]

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arXiv:2602.21365v1 Announce Type: new Abstract: Purpose: Curating large-scale datasets of operating room (OR) workflow, encompassing rare, safety-critical, or atypical events, remains operationally and ethically challenging. This data bottleneck complicates the development of ambient intelligence for detecting, understanding, and mitigating rare or safety-critical events in the OR. Methods: This work presents an OR video diffusion framework that enables controlled synthesis of rare and safety-critical events. The framework integrates a geometric abstraction module, a conditioning module, and a fine-tuned diffusion […]

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arXiv:2601.08065v1 Announce Type: new Abstract: Forward reachability analysis is the predominant approach for verifying reach-avoid properties in neural feedback systems (dynamical systems controlled by neural networks). This dominance stems from the limited scalability of existing backward reachability methods. In this work, we introduce new algorithms that compute both over- and under-approximations of backward reachable sets for such systems. We further integrate these backward algorithms with established forward analysis techniques to yield a unified verification framework for neural feedback […]

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