Here's your daily roundup of the most relevant AI and ML news for July 28, 2026. We're also covering 8 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. CausAdv: A Causal-based Framework for Detecting Adversarial Examples
arXiv:2411.00839v4 Announce Type: replace Abstract: Deep learning has led to tremendous success in computer vision, largely due to Convolutional Neural Networks (CNNs). However, CNNs have been shown to be vulnerable to crafted adversarial perturbations. This vulnerability of adversarial examples...
Source: arXiv - Machine Learning | 10 hours ago
2. Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs
arXiv:2607.22786v1 Announce Type: new Abstract: In this work, we explore how the inference time of a Transformer Neural Network can be efficiently optimized with applications to real-time anomaly detection in financial time series. The financial time series are price series such as asset prices....
Source: arXiv - Machine Learning | 10 hours ago
3. Deep learning approaches show promise for predicting childhood malnutrition: A comparative study with traditional machine learning methods using survey data
arXiv:2602.10381v2 Announce Type: replace Abstract: Childhood malnutrition remains a major public health concern in Nepal and other low-resource settings, while conventional case-finding approaches are labor-intensive and frequently unavailable in remote areas. This study provides one of the fir...
Source: arXiv - Machine Learning | 10 hours ago
4. Adversarial Style Optimization: Enhancing VLM Jailbreaks by GRPO-based Stylistic Triggers Optimization
arXiv:2607.21619v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved impressive performance, but their safety alignment remains vulnerable to jailbreak attacks. Existing content-based jailbreaks are often inconsistent and show unsatisfying performance against ...
Source: arXiv - AI | 10 hours ago
5. A New Kind of Adversarial Example: Measuring the Human-Model Gap, and Its Relationship to OOD Detection
arXiv:2607.22722v1 Announce Type: cross Abstract: Almost all adversarial attacks add an imperceptible perturbation to fool a model. We instead study the opposite: a large, clearly visible perturbation that causes the model to keep its original, correct prediction, even though a human would no lo...
Source: arXiv - Machine Learning | 10 hours ago
6. Opaque Epistemic Mediation: How LLM Deployment Configurations Shape the Validation of Pseudo-Science
arXiv:2607.22513v2 Announce Type: cross Abstract: Commercial large language models are increasingly used as knowledge references, yet their stance on contested scientific claims is neither stable nor transparent. We tested how four major LLM families (Claude, Grok, GPT, Gemini) evaluate ethnonat...
Source: arXiv - AI | 10 hours ago
7. Cross-Model LLM Code Review: Should you use Claude to review Codex or vice versa?
arXiv:2607.21656v1 Announce Type: cross Abstract: Developers increasingly use two coding agents together: one writes a draft, and the other reviews it. However, it is not clear whether the pairing is worth its cost and time, or whether the order of the pairing matters. We run a controlled experi...
Source: arXiv - AI | 10 hours ago
8. CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation
arXiv:2511.19711v2 Announce Type: replace-cross Abstract: MPC-based ML uses multi-party computation (MPC) to run machine learning (ML) workloads across multiple parties without each having to share their private data or model parameters. However, existing frameworks frequently degrade accuracy a...
Source: arXiv - AI | 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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