Here's your daily roundup of the most relevant AI and ML news for March 10, 2026. We're also covering 8 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. Adversarial Robustness of Partitioned Quantum Classifiers
arXiv:2502.20403v2 Announce Type: replace-cross Abstract: Adversarial robustness in quantum classifiers is a critical area of study, providing insights into their performance compared to classical models and uncovering potential advantages inherent to quantum machine learning. In the NISQ era of...
Source: arXiv - AI | 10 hours ago
2. Reasoned Safety Alignment: Ensuring Jailbreak Defense via Answer-Then-Check
arXiv:2509.11629v2 Announce Type: replace-cross Abstract: As large language models (LLMs) continue to advance in capabilities, ensuring their safety against jailbreak attacks remains a critical challenge. In this paper, we introduce a novel safety alignment approach called Answer-Then-Check, whi...
Source: arXiv - AI | 10 hours ago
3. Adversarial Domain Adaptation Enables Knowledge Transfer Across Heterogeneous RNA-Seq Datasets
arXiv:2603.08062v1 Announce Type: new Abstract: Accurate phenotype prediction from RNA sequencing (RNA-seq) data is essential for diagnosis, biomarker discovery, and personalized medicine. Deep learning models have demonstrated strong potential to outperform classical machine learning approaches...
Source: arXiv - Machine Learning | 10 hours ago
4. Depth Charge: Jailbreak Large Language Models from Deep Safety Attention Heads
arXiv:2603.05772v1 Announce Type: cross Abstract: Currently, open-sourced large language models (OSLLMs) have demonstrated remarkable generative performance. However, as their structure and weights are made public, they are exposed to jailbreak attacks even after alignment. Existing attacks oper...
Source: arXiv - AI | 10 hours ago
5. Adversarial Latent-State Training for Robust Policies in Partially Observable Domains
arXiv:2603.07313v1 Announce Type: new Abstract: Robustness under latent distribution shift remains challenging in partially observable reinforcement learning. We formalize a focused setting where an adversary selects a hidden initial latent distribution before the episode, termed an adversarial ...
Source: arXiv - Machine Learning | 10 hours ago
6. Hide and Find: A Distributed Adversarial Attack on Federated Graph Learning
arXiv:2603.07743v1 Announce Type: new Abstract: Federated Graph Learning (FedGL) is vulnerable to malicious attacks, yet developing a truly effective and stealthy attack method remains a significant challenge. Existing attack methods suffer from low attack success rates, high computational costs...
Source: arXiv - Machine Learning | 10 hours ago
7. The Struggle Between Continuation and Refusal: A Mechanistic Analysis of the Continuation-Triggered Jailbreak in LLMs
arXiv:2603.08234v1 Announce Type: cross Abstract: With the rapid advancement of large language models (LLMs), the safety of LLMs has become a critical concern. Despite significant efforts in safety alignment, current LLMs remain vulnerable to jailbreaking attacks. However, the root causes of suc...
Source: arXiv - Machine Learning | 10 hours ago
8. Generative Adversarial Regression (GAR): Learning Conditional Risk Scenarios
arXiv:2603.08553v1 Announce Type: cross Abstract: We propose Generative Adversarial Regression (GAR), a framework for learning conditional risk scenarios through generators aligned with downstream risk objectives. GAR builds on a regression characterization of conditional risk for elicitable fun...
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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