Here's your daily roundup of the most relevant AI and ML news for May 05, 2026. We're also covering 8 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. Sentra-Guard: A Real-Time Multilingual Defense Against Adversarial LLM Prompts
arXiv:2510.22628v2 Announce Type: replace-cross Abstract: This paper presents a real-time modular defense system named Sentra-Guard. The system detects and mitigates jailbreak and prompt injection attacks targeting large language models (LLMs). The framework uses a hybrid architecture with FAISS...
Source: arXiv - AI | 10 hours ago
2. LLM-VA: Resolving the Jailbreak-Overrefusal Trade-off via Vector Alignment
arXiv:2601.19487v2 Announce Type: replace Abstract: Safety-aligned LLMs suffer from two failure modes: jailbreak (answering harmful inputs) and over-refusal (declining benign queries). Existing vector steering methods adjust the magnitude of answer vectors, but this creates a fundamental trade-o...
Source: arXiv - Machine Learning | 10 hours ago
3. Almost for Free: Crafting Adversarial Examples with Convolutional Image Filters
arXiv:2605.01098v1 Announce Type: new Abstract: Adversarial examples in machine learning are typically generated using gradients, obtained either directly through access to the model or approximated via queries to it. In this paper, we propose a much simpler approach to craft adversarial example...
Source: arXiv - Machine Learning | 10 hours ago
4. Adversarial Imitation Learning with General Function Approximation: Theoretical Analysis and Practical Algorithms
arXiv:2605.01778v1 Announce Type: new Abstract: Adversarial imitation learning (AIL), a prominent approach in imitation learning, has achieved significant practical success powered by neural network approximation. However, existing theoretical analyses of AIL are primarily confined to simplified...
Source: arXiv - Machine Learning | 10 hours ago
5. Analyzing Adversarial Inputs in Deep Reinforcement Learning
arXiv:2402.05284v2 Announce Type: replace Abstract: In recent years, Deep Reinforcement Learning (DRL) has become a popular paradigm in machine learning due to its successful applications to real-world and complex systems. However, even the state-of-the-art DRL models have been shown to suffer f...
Source: arXiv - Machine Learning | 10 hours ago
6. ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineering
arXiv:2505.23723v2 Announce Type: replace-cross Abstract: The emergence of large language model (LLM)-based agents has significantly advanced the development of autonomous machine learning (ML) engineering. However, the dominant prompt-based paradigm exhibits limitations: smaller models lack the...
Source: arXiv - AI | 10 hours ago
7. 2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
arXiv:2605.00839v1 Announce Type: cross Abstract: The evolution of artificial intelligence (AI) and machine learning (ML) is reshaping smart manufacturing by providing new capabilities for efficiency, adaptability, and autonomy across industrial value chains. However, the deployment of AI and ML...
Source: arXiv - Machine Learning | 10 hours ago
8. Minimal, Local, Causal Explanations for Jailbreak Success in Large Language Models
arXiv:2605.00123v1 Announce Type: new Abstract: Safety trained large language models (LLMs) can often be induced to answer harmful requests through jailbreak prompts. Because we lack a robust understanding of why LLMs are susceptible to jailbreaks, future frontier models operating more autonomou...
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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