Here's your daily roundup of the most relevant AI and ML news for February 10, 2026. We're also covering 8 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. Exploring Adversarial Watermarking in Transformer-Based Models: Transferability and Robustness Against Defense Mechanism for Medical Images
arXiv:2506.06389v3 Announce Type: replace-cross Abstract: Deep learning models have shown remarkable success in dermatological image analysis, offering potential for automated skin disease diagnosis. Previously, convolutional neural network(CNN) based architectures have achieved immense populari...
Source: arXiv - Machine Learning | 18 hours ago
2. Implicit Strategic Optimization: Rethinking Long-Horizon Decision-Making in Adversarial Poker Environments
arXiv:2602.08041v1 Announce Type: cross Abstract: Training large language model (LLM) agents for adversarial games is often driven by episodic objectives such as win rate. In long-horizon settings, however, payoffs are shaped by latent strategic externalities that evolve over time, so myopic opt...
Source: arXiv - AI | 18 hours ago
3. SafeDialBench: A Fine-Grained Safety Evaluation Benchmark for Large Language Models in Multi-Turn Dialogues with Diverse Jailbreak Attacks
arXiv:2502.11090v4 Announce Type: replace-cross Abstract: With the rapid advancement of Large Language Models (LLMs), the safety of LLMs has been a critical concern requiring precise assessment. Current benchmarks primarily concentrate on single-turn dialogues or a single jailbreak attack method...
Source: arXiv - AI | 18 hours ago
4. Modalities, a PyTorch-native Framework For Large-scale LLM Training and Research
arXiv:2602.08387v1 Announce Type: new Abstract: Today's LLM (pre-) training and research workflows typically allocate a significant amount of compute to large-scale ablation studies. Despite the substantial compute costs of these ablations, existing open-source frameworks provide limited tooling...
Source: arXiv - Machine Learning | 18 hours ago
5. Efficient and Adaptable Detection of Malicious LLM Prompts via Bootstrap Aggregation
arXiv:2602.08062v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding, reasoning, and generation. However, these systems remain susceptible to malicious prompts that induce unsafe or policy-violating behavior thro...
Source: arXiv - Machine Learning | 18 hours ago
6. Incentive-Aware AI Safety via Strategic Resource Allocation: A Stackelberg Security Games Perspective
arXiv:2602.07259v1 Announce Type: new Abstract: As AI systems grow more capable and autonomous, ensuring their safety and reliability requires not only model-level alignment but also strategic oversight of the humans and institutions involved in their development and deployment. Existing safety ...
Source: arXiv - AI | 18 hours ago
7. A Comparative Study of Adversarial Robustness in CNN and CNN-ANFIS Architectures
arXiv:2602.07028v1 Announce Type: cross Abstract: Convolutional Neural Networks (CNNs) achieve strong image classification performance but lack interpretability and are vulnerable to adversarial attacks. Neuro-fuzzy hybrids such as DCNFIS replace fully connected CNN classifiers with Adaptive Neu...
Source: arXiv - AI | 18 hours ago
8. The Double-Edged Sword of Data-Driven Super-Resolution: Adversarial Super-Resolution Models
arXiv:2602.07251v1 Announce Type: cross Abstract: Data-driven super-resolution (SR) methods are often integrated into imaging pipelines as preprocessing steps to improve downstream tasks such as classification and detection. However, these SR models introduce a previously unexplored attack surfa...
Source: arXiv - AI | 18 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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