Here's your daily roundup of the most relevant AI and ML news for May 29, 2026. We're also covering 8 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. Quantum-Enhanced Adversarial Robustness in Artificial Intelligence
arXiv:2605.28899v1 Announce Type: cross Abstract: Artificial Intelligence has achieved remarkable success across diverse application domains. However, its vulnerability to adversarial attacks poses significant challenges to reliability, security, and trustworthiness. Adversarial machine learning...
Source: arXiv - AI | 10 hours ago
2. Measuring Real-World Prompt Injection Attacks in LLM-based Resume Screening
arXiv:2605.28999v1 Announce Type: cross Abstract: LLMs are vulnerable to prompt injection attacks. However, this vulnerability has been primarily demonstrated conceptually in academic studies or through a few anecdotal case studies. Its prevalence and impact in real-world LLM-based applications ...
Source: arXiv - Machine Learning | 10 hours ago
3. An Empirical Study of the Influence of Adversarial Fine-Tuning on Compressed Neural Networks
arXiv:2403.09441v3 Announce Type: replace Abstract: As deep learning (DL) models are increasingly being integrated into our everyday lives, ensuring their safety by making them robust against adversarial attacks has become increasingly critical. DL models have been found to be susceptible to adv...
Source: arXiv - Machine Learning | 10 hours ago
4. SciIntBench: Measuring LLM Compliance with Research Integrity Norms Under Adversarial Framing
arXiv:2605.29468v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to support scientific work, but it is unclear whether they uphold responsible conduct of research (RCR) norms or help undermine them. We introduce SciIntBench, an adversarial benchmark of 810 pro...
Source: arXiv - AI | 10 hours ago
5. SelfGrader: LLM Jailbreak Detection via Anchored Token-Level Logits
arXiv:2604.01473v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are powerful tools for answering user queries, yet they remain highly vulnerable to jailbreak attacks. Existing guardrail methods typically rely on internal features or textual responses to detect malicious qu...
Source: arXiv - AI | 10 hours ago
6. Improving Adversarial Robustness of Attribution via Implicit Regularization
arXiv:2605.29983v1 Announce Type: new Abstract: The adversarial robustness of attributions is a fundamental requirement for reliable explainability in deep learning, yet existing approaches typically rely on computationally expensive explicit regularization. In this work, we show that attributio...
Source: arXiv - Machine Learning | 10 hours ago
7. Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation
arXiv:2310.14161v2 Announce Type: replace Abstract: Machine learning has been successfully applied to improve the efficiency of Mixed-Integer Linear Programming (MILP) solvers. However, the learning-based solvers often suffer from severe performance degradation on unseen MILP instances -- especi...
Source: arXiv - Machine Learning | 10 hours ago
8. Overcoming Forgetting in LLM Fine-Tuning with Evolution Strategies
arXiv:2605.30148v1 Announce Type: new Abstract: Evolution Strategies (ES) has recently emerged as a competitive alternative to reinforcement learning (RL) for large language model (LLM) fine-tuning, offering advantages through simplicity, scalability, and inference-only training. However, recent...
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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