arXiv:2509.18674v1 Announce Type: cross Abstract: A scalable Bayesian machine learning framework is introduced for estimating scalar properties of an unknown quantum state from measurement data, which bypasses full density matrix reconstruction. This work is the first to integrate the classical…
arXiv:2509.18131v1 Announce Type: new Abstract: It is shown that the weight matrices of transformer-based machine learning applications to the solution of two representative physical applications show a random-like character which bears no directly recognizable link to the physical and mathematical…
arXiv:2509.18790v1 Announce Type: cross Abstract: Infrastructure as Code (IaC) automates the provisioning and management of IT infrastructure through scripts and tools, streamlining software deployment. Prior studies have shown that IaC scripts often contain recurring security misconfigurations, and several detection and…
arXiv:2509.18133v1 Announce Type: new Abstract: In real-world industrial settings, large language models (LLMs) must learn continually to keep pace with diverse and evolving tasks, requiring self-evolution to refine knowledge under dynamic data distributions. However, existing continual learning (CL) approaches, such…
arXiv:2509.18906v1 Announce Type: cross Abstract: This paper introduces a novel framework for Edge Inference (EI) that bypasses the conventional practice of treating the wireless channel as noise. We utilize Stacked Intelligent Metasurfaces (SIMs) to control wireless propagation, enabling the channel…
arXiv:2509.18134v1 Announce Type: new Abstract: This paper investigates the privacy-preserving distributed optimization problem, aiming to protect agents’ private information from potential attackers during the optimization process. Gradient tracking, an advanced technique for improving the convergence rate in distributed optimization, has…
arXiv:2509.19117v1 Announce Type: cross Abstract: Large language models (LLMs) excel in many tasks of software engineering, yet progress in leveraging them for vulnerability discovery has stalled in recent years. To understand this phenomenon, we investigate LLMs through the lens of…
arXiv:2509.18135v1 Announce Type: new Abstract: Inter-series correlations are crucial for accurate multivariate time series forecasting, yet these relationships often exhibit complex dynamics across different temporal scales. Existing methods are limited in modeling these multi-scale dependencies and struggle to capture their…
arXiv:2411.08019v2 Announce Type: replace-cross Abstract: In this work, we present sequence-driven structural causal models (SD-SCMs), a framework for specifying causal models with user-defined structure and language-model-defined mechanisms. We characterize how an SD-SCM enables sampling from observational, interventional, and counterfactual distributions…
arXiv:2503.23329v2 Announce Type: replace Abstract: Misinformation spans various domains, but detection methods trained on specific domains often perform poorly when applied to others. With the rapid development of Large Language Models (LLMs), researchers have begun to utilize LLMs for cross-domain…
