arXiv:2502.10328v4 Announce Type: replace Abstract: Markov Chain Monte Carlo (MCMC) algorithms are essential tools in computational statistics for sampling from unnormalised probability distributions, but can be fragile when targeting high-dimensional, multimodal, or complex target distributions. Parallel Tempering (PT) enhances MCMC’s sample efficiency through annealing and parallel computation, propagating samples from tractable reference distributions to intractable targets via state swapping across interpolating distributions. The effectiveness of PT is limited by the often minimal overlap between adjacent distributions in challenging […]

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arXiv:2604.08632v1 Announce Type: new Abstract: Network segmentation is a foundational enterprise security control. Despite its recognized benefits, segmentation initiatives frequently fail in practice, and the field lacks a systematic empirical explanation for why these projects do not achieve their intended outcomes. This paper presents an empirical study of failed segmentation projects based on a survey of 400 U.S.-based network security practitioners. The survey was grounded in a two-part failure framework that separately measures general IT project failure factors […]

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Reinforcement Learning (RL) agents often struggle in real-world applications where environmental conditions are non-stationary, particularly when reward functions shift or the available action space expands. This paper introduces MORPHIN, a self-adaptive Q-learning framework that enables on-the-fly adaptation without full retraining. By integrating concept drift detection with dynamic adjustments to learning and exploration hyperparameters, MORPHIN adapts agents to changes in both the reward function and on-the-fly expansions of the agent’s action space, while preserving prior policy knowledge to prevent […]

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arXiv:2603.28843v1 Announce Type: new Abstract: We revisit the complexity of verifying basic identities, such as associativity and distributivity, on a given finite algebraic structure. In particular, while Rajagopalan and Schulman (FOCS’96, SICOMP’00) gave a surprising randomized algorithm to verify associativity of an operation $odot: Stimes Sto S$ in optimal time $O(|S|^2)$, they left the open problem of finding any subcubic algorithm for verifying distributivity of given operations $odot,oplus: Stimes Sto S$. Our results are as follows: * We […]

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Causal artificial intelligence aims to enhance explainability, trustworthiness, and robustness in AI by leveraging structural causal models (SCMs). In this pursuit, recent advances formalize network sheaves and cosheaves of causal knowledge. Pushing in the same direction, we tackle the learning of consistent causal abstraction network (CAN), a sheaf-theoretic framework where (i) SCMs are Gaussian, (ii) restriction maps are transposes of constructive linear causal abstractions (CAs) adhering to the semantic embedding principle, and (iii) edge stalks correspond–up to permutation–to […]

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arXiv:2601.08887v1 Announce Type: new Abstract: Efficient load-balancing mechanisms are critical for maximizing performance and increasing the quality of service (QoS) of data center networks (DCNs). Obtaining the optimal QoS while minimizing resource consumption remains a significant challenge. This paper proposes the streamlined pathway (SP) model, which is a flow scheduling solution that requires minimal statistical knowledge of the DCN data plane. The SP model utilizes the software-defined networks (SDN) paradigm with less information gathered from the DCN data […]

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arXiv:2603.03485v1 Announce Type: new Abstract: Recent video diffusion models have achieved impressive capabilities as large-scale generative world models. However, these models often struggle with fine-grained physical consistency, exhibiting physically implausible dynamics over time. In this work, we present textbf{Phys4D}, a pipeline for learning physics-consistent 4D world representations from video diffusion models. Phys4D adopts textbf{a three-stage training paradigm} that progressively lifts appearance-driven video diffusion models into physics-consistent 4D world representations. We first bootstrap robust geometry and motion representations through […]

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The Invisible Waste of the Centralized Cloud As we head into 2026, a staggering and largely ignored crisis is unfolding in the world of technology: the massive waste inherent in centralized cloud infrastructure. Enterprises are currently wasting upwards of $44 billion on cloud spending due to a legacy model of over-provisioning, where organizations pay for peak capacity that they rarely use. This waste isn’t just a financial burden; it’s a resource crisis. Centralized data centers consume vast amounts […]

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arXiv:2604.02344v1 Announce Type: new Abstract: WebGPU’s security-focused design imposes per-operation validation that compounds across the many small dispatches in neural network inference, yet the true cost of this overhead is poorly characterized. We present a systematic characterization of WebGPU dispatch overhead for LLM inference at batch size 1, spanning four GPU vendors (NVIDIA, AMD, Apple, Intel), two native implementations (Dawn, wgpu-native) and three browsers (Chrome, Safari, Firefox), and two model sizes (Qwen2.5-0.5B and 1.5B). Our primary contribution is […]

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Partial differential equations (PDEs) govern physical phenomena across the full range of scientific scales, yet their computational solution remains one of the defining challenges of modern science. This critical review examines two mature but epistemologically distinct paradigms for PDE solution, classical numerical methods and machine learning approaches, through a unified evaluative framework organized around six fundamental computational challenges. Classical methods are assessed for their structure-preserving properties, rigorous convergence theory, and scalable solver design; their persistent limitations in high-dimensional […]

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