arXiv:2603.24597v1 Announce Type: new Abstract: We study algorithmic barriers to detecting and repairing a systematic form of structural overspecification in adaptive data-structure selection. An input instance induces an implied workload signature, such as ordering, sparsity, dynamism, locality, or substring structure, and candidate implementations may be preferred because they match that full signature even when the measured workload evidence supports only a strict subset of it. Under a model in which pairwise evaluators favor implementations that realize the implied […]

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arXiv:2602.15091v2 Announce Type: replace Abstract: Mixture-of-Experts (MoE) architectures decompose prediction tasks into specialized expert sub-networks selected by a gating mechanism. This letter adopts a communication-theoretic view of MoE gating, modeling the gate as a stochastic channel operating under a finite information rate. Within an information-theoretic learning framework, {we specialize a mutual-information generalization bound and develop a rate-distortion characterization $D(R_g)$ of finite-rate gating, where $R_g:=I(X; T)$, yielding (under a standard empirical rate-distortion optimality condition) $mathbb{E}[R(W)] le D(R_g)+delta_m+sqrt{(2/m), I(S; W)}$. […]

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Miami, Florida, January 22nd, 2026/CyberNewsWire/–Halo Security, a leading provider of external attack surface management and penetration testing services, today announced it has successfully achieved SOC 2 Type II compliance following an extensive multi-month audit by Insight Assurance. This certification validates that Halo Security’s security controls are not only properly designed but also operate effectively and consistently over time. “SOC 2 Type II compliance demonstrates our unwavering commitment to protecting customer data through proven, operational security practices,” said Lisa […]

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arXiv:2604.02393v1 Announce Type: new Abstract: Vanishing gradient and overfitting are two of the most extensively studied problems in the literature about machine learning. However, they are frequently considered in some asymptotic setting, which obscure the underlying dynamical mechanisms responsible for their emergence. In this paper, we aim to provide a clear dynamical description of learning in multi-layer perceptrons. To this end, we introduce a minimal model, inspired by studies by Fukumizu and Amari, to investigate vanishing gradients and […]

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Downstream adaptation of a contrastively pretrained vision–language model can improve in-domain accuracy while degrading performance on unseen transfer tasks. This study examines how full fine-tuning and low-rank adaptation alter attention heatmaps under a controlled design that matches learning rate across adaptation methods. The completed matched-learning-rate matrix contains 80 runs using the OpenAI Contrastive Language–Image Pretraining model with a base 32-patch vision transformer image encoder, two datasets (EuroSAT and Oxford-IIIT Pets), four shared learning rates (1e-6, 5e-6, 1e-5, and […]

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arXiv:2407.02700v4 Announce Type: replace-cross Abstract: This paper presents a novel approach for the output range estimation problem in Deep Neural Networks (DNNs) by integrating a Simulated Annealing (SA) algorithm tailored to operate within constrained domains and ensure convergence towards global optima. The method effectively addresses the challenges posed by the lack of local geometric information and the high non-linearity inherent to DNNs, making it applicable to a wide variety of architectures, with a special focus on Residual Networks […]

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arXiv:2602.21341v1 Announce Type: new Abstract: Geometry-free view synthesis transformers have recently achieved state-of-the-art performance in Novel View Synthesis (NVS), outperforming traditional approaches that rely on explicit geometry modeling. Yet the factors governing their scaling with compute remain unclear. We present a systematic study of scaling laws for view synthesis transformers and derive design principles for training compute-optimal NVS models. Contrary to prior findings, we show that encoder-decoder architectures can be compute-optimal; we trace earlier negative results to suboptimal […]

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