arXiv:2602.15239v1 Announce Type: new Abstract: Transformers have achieved remarkable success across domains, motivating the rise of Graph Transformers (GTs) as attention-based architectures for graph-structured data. A key design choice in GTs is the use of Graph Neural Network (GNN)-based positional encodings to incorporate structural information. In this work, we study GTs through the lens of manifold limit models for graph sequences and establish a theoretical connection between GTs with GNN positional encodings and Manifold Neural Networks (MNNs). Building […]

Ver mais

arXiv:2603.26788v1 Announce Type: new Abstract: Zero-shot object navigation requires agents to locate unseen target objects in unfamiliar environments without prior maps or task-specific training which remains a significant challenge. Although recent advancements in vision-language models(VLMs) provide promising commonsense reasoning capabilities for this task, these models still suffer from spatial hallucinations, local exploration deadlocks, and a disconnect between high-level semantic intent and low-level control. In this regard, we propose a novel hierarchical navigation framework named ReMemNav, which seamlessly integrates […]

Ver mais

The Extended Riemann Hypothesis (for Dedekind zeta functions), the Generalized Riemann Hypothesis (for Dirichlet L-functions), and the Grand Riemann Hypothesis (for all L-functions) are proved under a unified framework. As a consequence, Landau–Siegel zeros are excluded; that is, the Landau–Siegel zeros conjecture is justified.

Ver mais

arXiv:2603.12459v1 Announce Type: new Abstract: We present an alternative way of solving the steerable kernel constraint that appears in the design of steerable equivariant convolutional neural networks. We find explicit real and complex bases which are ready to use, for different symmetry groups and for feature maps of arbitrary tensor type. A major advantage of this method is that it bypasses the need to numerically or analytically compute Clebsch-Gordan coefficients and works directly with the representations of the […]

Ver mais

arXiv:2602.03965v1 Announce Type: new Abstract: Latency anomalies, defined as persistent or transient increases in round-trip time (RTT), are common in residential Internet performance. When multiple users observe anomalies to the same destination, this may reflect shared infrastructure, routing behavior, or congestion. Inferring such shared behavior is challenging because anomaly magnitudes vary widely across devices, even within the same ISP and geographic area, and detailed network topology information is often unavailable. We study whether devices experiencing a shared latency […]

Ver mais

arXiv:2602.17668v1 Announce Type: new Abstract: Manual notes and scattered messaging applications used in managing business processes compromise data integrity and abstract project tracking. In this study, an integrated system that works simultaneously on web and mobile platforms has been developed to enable individual users and teams to manage their workflows with concrete data. The system architecture integrates MongoDB, which stores data in JSON format, Node.js Express.js on the server side, React.js on the web interface, and React Native […]

Ver mais

Feature learning strength (FLS), i.e., the inverse of the effective output scaling of a model, plays a critical role in shaping the optimization dynamics of neural nets. While its impact has been extensively studied under the asymptotic regimes — both in training time and FLS — existing theory offers limited insight into how FLS affects generalization in practical settings, such as when training is stopped upon reaching a target training risk. In this work, we investigate the impact […]

Ver mais

arXiv:2602.15837v1 Announce Type: new Abstract: Autonomous driving systems (ADS) are safety-critical and require rigorous testing before public deployment. Simulation-based scenario testing provides a safe and cost-effective alternative to extensive on-road trials, enabling efficient evaluation of ADS under diverse and high-risk conditions. However, existing approaches mainly evaluates the scenarios based on their proximity to collisions and focus on scenarios already close to collision, leaving many other hazardous situations unexplored. To bridge this, we introduce a collision-related concept of conflict […]

Ver mais

Cloud cost and system reliability are the same problem viewed through different instruments. Cost anomalies surface bugs, retry storms, and memory leaks before they cause outages — if you’re watching. The fix: embed billing telemetry into your observability stack, enforce resource tagging at the pipeline level, write anomaly-based cost alerts, and treat budget overruns as budget burns the same way you treat SLO violations. Manage them separately and you’ll always be six weeks behind the failure that caused […]

Ver mais