Here's your daily roundup of the most relevant AI and ML news for May 22, 2026. We're also covering 7 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. FT-Dojo: Towards Autonomous LLM Fine-Tuning with Language Agents
arXiv:2603.01712v2 Announce Type: replace Abstract: Fine-tuning large language models for vertical domains remains labor-intensive, requiring practitioners to curate data, configure training, and iteratively diagnose model behavior. Despite growing interest in autonomous machine learning and lan...
Source: arXiv - AI | 10 hours ago
2. A Comparative Study of Machine Learning and Deep Learning for Out-of-Distribution Detection
arXiv:2605.10181v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection is essential for building reliable AI systems, as models that produce outputs for invalid inputs cannot be trusted. Although deep learning (DL) is often assumed to outperform traditional machine learnin...
Source: arXiv - AI | 10 hours ago
3. An exponential mechanism based on quadratic approximations for fine-tuning machine learning models with privacy guarantees
arXiv:2605.20521v1 Announce Type: new Abstract: Fine-tuning adapts a pretrained machine learning model to a small, sensitive dataset, but this process risks memorizing individual new data points, making the model vulnerable to adversaries who seek to extract sensitive information. In this work, ...
Source: arXiv - Machine Learning | 10 hours ago
4. REFLECTOR: Internalizing Step-wise Reflection against Indirect Jailbreak
arXiv:2605.20654v1 Announce Type: cross Abstract: While Large Language Models (LLMs) demonstrate remarkable capabilities, they remain susceptible to sophisticated, multi-step jailbreak attacks that circumvent conventional surface-level safety alignment by exploiting the internal generation proce...
Source: arXiv - AI | 10 hours ago
5. Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detection
arXiv:2605.21164v1 Announce Type: new Abstract: Credit card fraud detection is fundamentally challenged by extreme class imbalance, where fraudulent transactions are rare yet operationally critical. This imbalance often biases supervised learners toward the legitimate class, leading to high over...
Source: arXiv - Machine Learning | 10 hours ago
6. Adversarial Robustness in One-Stage Learning-to-Defer
arXiv:2510.10988v2 Announce Type: replace-cross Abstract: Learning-to-Defer (L2D) enables hybrid decision-making by routing inputs either to a predictor or to external experts. While promising, L2D is highly vulnerable to adversarial perturbations, which can not only flip predictions but also ma...
Source: arXiv - Machine Learning | 10 hours ago
7. OSCToM: RL-Guided Adversarial Generation for High-Order Theory of Mind
arXiv:2605.20423v1 Announce Type: new Abstract: Large Language Models (LLMs) perform well on many language tasks, but their Theory of Mind (ToM) reasoning is still uneven in complex social settings. Existing benchmarks, including ExploreToM, do not always test the recursive beliefs and informati...
Source: arXiv - AI | 10 hours ago
Tech & Development
8. Building Context-Aware Search in Python with LLM Embeddings and Metadata
Article URL: https://machinelearningmastery.com/building-context-aware-search-in-python-with-llm-embeddings-metadata/ Comments URL: https://news.ycombinator.com/item?id=48235888 Points: 1
Comments: 0
Source: Hacker News - AI | just now
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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