Here's your daily roundup of the most relevant AI and ML news for May 18, 2026. Today's digest includes 1 security-focused story. We're also covering 7 research developments. Click through to read the full articles from our curated sources.
Security & Safety
1. Mistral Developing New AI Model for Banks Lacking Mythos Access
Article URL: https://www.bloomberg.com/news/articles/2026-05-13/mistral-developing-new-ai-model-for-banks-lacking-mythos-access Comments URL: https://news.ycombinator.com/item?id=48179291 Points: 4
Comments: 0
Source: Hacker News - ML Security | just now
Research & Papers
2. Context, Reasoning, and Hierarchy: A Cost-Performance Study of Compound LLM Agent Design in an Adversarial POMDP
arXiv:2605.16205v1 Announce Type: cross Abstract: Deploying compound LLM agents in adversarial, partially observable sequential environments requires navigating several design dimensions: (1) what the agent sees, (2) how it reasons, and (3) how tasks are decomposed across components. Yet practit...
Source: arXiv - Machine Learning | 10 hours ago
3. When AI Persuades: Adversarial Explanation Attacks on Human Trust in AI-Assisted Decision Making
arXiv:2602.04003v3 Announce Type: replace Abstract: Most adversarial threats in artificial intelligence (AI) target the computational behavior of models rather than the humans who rely on them. Yet modern AI systems increasingly operate within human decision loops, where users interpret and act ...
Source: arXiv - AI | 10 hours ago
4. TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination
arXiv:2605.15207v1 Announce Type: new Abstract: Multi-agent LLM systems have shown promise for complex reasoning, yet recent evaluations reveal they often underperform single-model baselines. We identify a structural failure mode in sequential fine-tuning of shared-context teams: updating one ag...
Source: arXiv - Machine Learning | 10 hours ago
5. Representation Without Reward: A JEPA Audit for LLM Fine-Tuning
arXiv:2605.15394v1 Announce Type: new Abstract: Joint-embedding predictive architectures (JEPAs) propose that a model should learn more useful abstractions when trained to predict latent representations rather than observed outputs. For autoregressive language-model fine-tuning the principle ent...
Source: arXiv - Machine Learning | 10 hours ago
6. When and Why Adversarial Training Improves PINNs: A Neural Tangent Kernel Perspective
arXiv:2605.15959v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) are powerful surrogates for differential equations but are notoriously difficult to train due to spectral bias, stiffness, and poor accuracy on high-frequency or multiscale solutions. Adversarial training ba...
Source: arXiv - Machine Learning | 10 hours ago
7. FlipAttack: Jailbreak LLMs via Flipping
arXiv:2410.02832v2 Announce Type: replace-cross Abstract: This paper proposes a simple yet effective jailbreak attack named FlipAttack against black-box LLMs. First, from the autoregressive nature, we reveal that LLMs tend to understand the text from left to right and find that they struggle to ...
Source: arXiv - AI | 10 hours ago
8. From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery
arXiv:2605.15412v1 Announce Type: cross Abstract: Modern quantitative trading increasingly relies on systematic models to extract predictive signals from large-scale financial data, where alpha factor discovery plays a central role in transforming market observations into tradable signals. Recen...
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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