Here's your daily roundup of the most relevant AI and ML news for April 24, 2026. We're also covering 8 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. Secure LLM Fine-Tuning via Safety-Aware Probing
arXiv:2505.16737v2 Announce Type: replace 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-training or ...
Source: arXiv - Machine Learning | 10 hours ago
2. 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 - Machine Learning | 10 hours ago
3. TREX: Automating LLM Fine-tuning via Agent-Driven Tree-based Exploration
arXiv:2604.14116v2 Announce Type: replace Abstract: While Large Language Models (LLMs) have empowered AI research agents to perform isolated scientific tasks, automating complex, real-world workflows, such as LLM training, remains a significant challenge. In this paper, we introduce TREX, a mult...
Source: arXiv - AI | 10 hours ago
4. Talking to a Know-It-All GPT or a Second-Guesser Claude? How Repair reveals unreliable Multi-Turn Behavior in LLMs
arXiv:2604.19245v2 Announce Type: replace-cross Abstract: Repair, an important resource for resolving trouble in human-human conversation, remains underexplored in human-LLM interaction. In this study, we investigate how LLMs engage in the interactive process of repair in multi-turn dialogues ar...
Source: arXiv - AI | 10 hours ago
5. Evaluating Post-hoc Explanations of the Transformer-based Genome Language Model DNABERT-2
arXiv:2604.21690v1 Announce Type: new Abstract: Explaining deep neural network predictions on genome sequences enables biological insight and hypothesis generation-often of greater interest than predictive performance alone. While explanations of convolutional neural networks (CNNs) have been sh...
Source: arXiv - Machine Learning | 10 hours ago
6. RIFT: Repurposing Negative Samples via Reward-Informed Fine-Tuning
arXiv:2601.09253v2 Announce Type: replace 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 propose R...
Source: arXiv - Machine Learning | 10 hours ago
7. OpInf-LLM: Parametric PDE Solving with LLMs via Operator Inference
arXiv:2602.01493v2 Announce Type: replace 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 PDEs ...
Source: arXiv - Machine Learning | 10 hours ago
8. Reversible Deep Learning for 13C NMR in Chemoinformatics: On Structures and Spectra
arXiv:2602.03875v4 Announce Type: replace Abstract: We introduce a reversible deep learning model for 13C NMR that uses a single conditional invertible neural network for both directions between molecular structures and spectra. The network is built from i-RevNet style bijective blocks, so the f...
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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