Here's your daily roundup of the most relevant AI and ML news for April 25, 2026. We're also covering 8 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. Logic Jailbreak: Efficiently Unlocking LLM Safety Restrictions Through Formal Logical Expression
arXiv:2505.13527v3 Announce Type: replace-cross Abstract: Despite substantial advancements in aligning large language models (LLMs) with human values, current safety mechanisms remain susceptible to jailbreak attacks. We hypothesize that this vulnerability stems from distributional discrepancies...
Source: arXiv - AI | 10 hours ago
2. Secure LLM Fine-Tuning via Safety-Aware Probing
arXiv:2505.16737v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved remarkable success across many applications, but their ability to generate harmful content raises serious safety concerns. Although safety alignment techniques are often applied during pre-traini...
Source: arXiv - AI | 10 hours ago
3. Adversarial Evasion in Non-Stationary Malware Detection: Minimizing Drift Signals through Similarity-Constrained Perturbations
arXiv:2604.21310v1 Announce Type: cross Abstract: Deep learning has emerged as a powerful approach for malware detection, demonstrating impressive accuracy across various data representations. However, these models face critical limitations in real-world, non-stationary environments where both m...
Source: arXiv - AI | 10 hours ago
4. AISafetyBenchExplorer: A Metric-Aware Catalogue of AI Safety Benchmarks Reveals Fragmented Measurement and Weak Benchmark Governance
arXiv:2604.12875v2 Announce Type: replace Abstract: The rapid expansion of large language model (LLM) safety evaluation has produced a substantial benchmark ecosystem, but not a correspondingly coherent measurement ecosystem. We present AISafetyBenchExplorer, a structured catalogue of 195 AI saf...
Source: arXiv - AI | 10 hours ago
5. Intent Laundering: AI Safety Datasets Are Not What They Seem
arXiv:2602.16729v3 Announce Type: replace-cross Abstract: We systematically evaluate the quality of widely used adversarial safety datasets from two perspectives: in isolation and in practice. In isolation, we examine how well these datasets reflect real-world adversarial attacks based on three ...
Source: arXiv - AI | 10 hours ago
6. Align Generative Artificial Intelligence with Human Preferences: A Novel Large Language Model Fine-Tuning Method for Online Review Management
arXiv:2604.21209v1 Announce Type: new Abstract: Online reviews have played a pivotal role in consumers' decision-making processes. Existing research has highlighted the significant impact of managerial review responses on customer relationship management and firm performance. However, a large po...
Source: arXiv - AI | 10 hours ago
7. RIFT: Repurposing Negative Samples via Reward-Informed Fine-Tuning
arXiv:2601.09253v2 Announce Type: replace-cross Abstract: While Supervised Fine-Tuning (SFT) and Rejection Sampling Fine-Tuning (RFT) are standard for LLM alignment, they either rely on costly expert data or discard valuable negative samples, leading to data inefficiency. To address this, we pro...
Source: arXiv - AI | 10 hours ago
8. OpInf-LLM: Parametric PDE Solving with LLMs via Operator Inference
arXiv:2602.01493v2 Announce Type: replace-cross Abstract: Solving diverse partial differential equations (PDEs) is fundamental in science and engineering. Large language models (LLMs) have demonstrated strong capabilities in code generation, symbolic reasoning, and tool use, but reliably solving...
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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