Here's your daily roundup of the most relevant AI and ML news for April 03, 2026. We're also covering 8 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. A Residual Guided strategy with Generative Adversarial Networks in training Physics-Informed Transformer Networks
arXiv:2508.00855v2 Announce Type: replace Abstract: Nonlinear partial differential equations (PDEs) are pivotal in modeling complex physical systems, yet traditional Physics-Informed Neural Networks (PINNs) often struggle with unresolved residuals in critical spatiotemporal regions and violation...
Source: arXiv - Machine Learning | 10 hours ago
2. AEGIS: Adversarial Entropy-Guided Immune System -- Thermodynamic State Space Models for Zero-Day Network Evasion Detection
arXiv:2604.02149v1 Announce Type: cross Abstract: As TLS 1.3 encryption limits traditional Deep Packet Inspection (DPI), the security community has pivoted to Euclidean Transformer-based classifiers (e.g., ET-BERT) for encrypted traffic analysis. However, these models remain vulnerable to byte-l...
Source: arXiv - Machine Learning | 10 hours ago
3. Towards Trustworthy Wi-Fi CSI-based Sensing: Systematic Evaluation of Adversarial Robustness
arXiv:2511.20456v2 Announce Type: replace Abstract: Machine learning drives Channel State Information (CSI)-based human sensing in modern wireless networks, enabling applications like device-free human activity recognition (HAR) and identification (HID). However, the susceptibility of these mode...
Source: arXiv - Machine Learning | 10 hours ago
4. InvZW: Invariant Feature Learning via Noise-Adversarial Training for Robust Image Zero-Watermarking
arXiv:2506.20370v2 Announce Type: replace-cross Abstract: This paper introduces a novel deep learning framework for robust image zero-watermarking based on distortion-invariant feature learning. As a zero-watermarking scheme, our method leaves the original image unaltered and learns a reference ...
Source: arXiv - Machine Learning | 10 hours ago
5. Adversarial Moral Stress Testing of Large Language Models
arXiv:2604.01108v1 Announce Type: new Abstract: Evaluating the ethical robustness of large language models (LLMs) deployed in software systems remains challenging, particularly under sustained adversarial user interaction. Existing safety benchmarks typically rely on single-round evaluations and...
Source: arXiv - AI | 10 hours ago
6. Bypassing Prompt Injection Detectors through Evasive Injections
arXiv:2602.00750v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used in interactive and retrieval-augmented systems, but they remain vulnerable to prompt injection attacks, where injected secondary prompts force the model to deviate from the user's instruc...
Source: arXiv - AI | 10 hours ago
7. On the Adversarial Robustness of Learning-based Conformal Novelty Detection
arXiv:2510.00463v4 Announce Type: replace-cross Abstract: This paper studies the adversarial robustness of conformal novelty detection. In particular, we focus on two powerful learning-based frameworks that come with finite-sample false discovery rate (FDR) control: one is AdaDetect (by Marandon...
Source: arXiv - Machine Learning | 10 hours ago
8. Graph-Informed Adversarial Modeling: Infimal Subadditivity of Interpolative Divergences
arXiv:2603.20025v2 Announce Type: replace-cross Abstract: We study adversarial learning when the target distribution factorizes according to a known Bayesian network. For interpolative divergences, including $(f,\Gamma)$-divergences, we prove a new infimal subadditivity principle showing that, u...
Source: arXiv - Machine Learning | 10 hours ago
About This Digest
This digest is automatically curated from leading AI and tech news sources, filtered for relevance to AI security and the ML ecosystem. Stories are scored and ranked based on their relevance to model security, supply chain safety, and the broader AI landscape.
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