Here's your daily roundup of the most relevant AI and ML news for July 01, 2026. Today's digest includes 1 security-focused story. We're also covering 6 research developments. Click through to read the full articles from our curated sources.
Security & Safety
1. Anthropic Restores Claude Fable 5 After U.S. Lifts Jailbreak-Linked Export Controls
Anthropic is putting Claude Fable 5 back online worldwide. On June 30, the U.S. Commerce Department lifted the export controls it had imposed on Fable and its more tightly controlled sibling Mythos 5 about two and a half weeks earlier.
Fable 5 returns to users on Wednesday, July 1, across C...
Source: The Hacker News (Security) | 7 hours ago
Research & Papers
2. Improving Certified Robustness via Adversarial Distillation
arXiv:2606.31653v1 Announce Type: new Abstract: Certified training aims to produce models whose predictions can be formally verified against adversarial perturbations, typically by optimising upper bounds on the worst-case loss over an allowed perturbation set. For neural networks, certified tra...
Source: arXiv - Machine Learning | 10 hours ago
3. A swap-adversarial framework for improving domain generalization in electrocorticography-based Parkinson's disease classification
arXiv:2602.10528v2 Announce Type: replace Abstract: We propose a novel swap-adversarial framework that mitigates high inter-subject variability and the high-dimensional low-sample-size problem in electrocorticography (ECoG) data. It achieves robust domain generalization across ECoG and electroen...
Source: arXiv - Machine Learning | 10 hours ago
4. A Complete Characterization of Learnability for Adversarial Noisy Bandits
arXiv:2605.09200v2 Announce Type: replace Abstract: We study adversarial noisy bandits given a known function class $\mathcal{F}$. In each round, the adversary selects a function $f \in \mathcal{F}$, the learner chooses an arm, and then observes a noisy reward determined by the chosen arm and th...
Source: arXiv - Machine Learning | 10 hours ago
5. Security--Fidelity Tradeoffs: The Hidden Cost of Prompt Injection Defense
arXiv:2606.30783v1 Announce Type: cross Abstract: We identify a security-fidelity tradeoff in defending LLMs against indirect prompt injection: defenses resist injected instructions largely by suppressing untrusted text, which corrupts tasks that must preserve it, such as translation and documen...
Source: arXiv - AI | 10 hours ago
6. An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU
arXiv:2603.16428v2 Announce Type: replace-cross Abstract: Fine-tuning Large Language Models (LLMs) has become essential for domain adaptation, but its memory-intensive property exceeds the capabilities of most GPUs. To address this challenge and democratize LLM fine-tuning, we present SlideForme...
Source: arXiv - AI | 10 hours ago
7. Mixture-of-Control: State-Aware Fine-Tuning for Transformer-based Models
arXiv:2606.31397v1 Announce Type: new Abstract: State-based fine-tuning has emerged as a compelling alternative to weight-based adaptation for transformers, updating lightweight controls into states rather than model weights, offering substantial memory savings while retaining parameter efficien...
Source: arXiv - Machine Learning | 10 hours ago
Industry News
8. Anthropic launches Claude Sonnet 5 as a cheaper way to run agents
Anthropic’s Claude Sonnet 5 brings stronger agentic capabilities, lower pricing, and improved safety, positioning the model as a cheaper alternative to Opus, GPT-5.5, and Gemini Pro.
Source: TechCrunch - AI | 20 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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