It's to ensure compliance with a government directive citing national security concerns. Anthroic has disabled all of its customers' access to Fable 5 and Mythos 5 in order to ensure compliance with an order it received from the government on Friday, June 12. All its other models and its Claude chatbot are not affected. The company said in its announcement that the US government wanted it to suspend all foreign nationals' access to its newly launched AI models, whether they're inside or outside the US and even if they're Anthropic employees, citing national security concerns. While the US government didn't specify those concerns, Anthropic believes that it's because the government heard about a method of jailbreaking Fable 5.

Large language models (LLMs) have demonstrated exceptional performance in reasoning tasks such as mathematics and coding, matching or surpassing human capabilities. However, these impressive reasoning abilities face significant challenges in specialized domains. Taking Go as an example, although AlphaGo has established the high performance ceiling of AI systems in Go, mainstream LLMs still struggle to reach even beginner-level proficiency, let alone perform natural language reasoning. This performance gap between general-purpose LLMs and domain experts is significantly limiting the application of LLMs on a wider range of domain-specific tasks. In this work, we aim to bridge the divide between LLMs' general reasoning capabilities and expert knowledge in domain-specific tasks.

While recent multimodal large language models (MLLMs) have advanced automated ECG interpretation, they still face two key limitations: (1) insufficient multimodal synergy between ECG time series and ECG images, and (2) limited explainability in linking diagnoses to granular waveform evidence. We introduce GEM, the first MLLM unifying ECG time series, 12-lead ECG images and text for grounded and clinician-aligned ECG interpretation. GEM enables feature-grounded analysis, evidence-driven reasoning, and a clinician-like diagnostic process through three core innovations: a dual-encoder framework extracting complementary time series and image features, cross-modal alignment for effective multimodal understanding, and knowledge-guided instruction data generation for generating high-granularity grounding data (ECG-Grounding) linking diagnoses to measurable parameters ($e.g.$, QRS/PR Intervals). Additionally, we propose the Grounded ECG Understanding task, a clinically motivated benchmark designed to comprehensively assess the MLLM's capability in grounded ECG understanding. Experimental results on both existing and our proposed benchmarks show GEM significantly improves predictive performance (CSN $7.4\%$ $\uparrow$), explainability ($22.7\%$

Petra Urban's sketch (before it was manipulated by AI) of the Syrian brothers jailed in January 2026 for murdering their sister. The PVV changed the image and used it on social media. Petra Urban's sketch (before it was manipulated by AI) of the Syrian brothers jailed in January 2026 for murdering their sister. The PVV changed the image and used it on social media. Geert Wilders' PVV altered sketch of jailed Syrian brothers to make them look more menacing A Dutch court artist has received damages after an MP for the far-right Party for Freedom (PVV) used one of her drawings without permission and manipulated it with AI to make the subjects look more menacing.

Anthropic has suspended its powerful new AI model after US authorities raised security concerns just days following its public release. In a statement published on its website, Anthropic said it was ordered to suspend foreign nationals from using Claude Fable 5, a program that the company self-described as too powerful. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance, the company wrote. Anthropic and the Trump administration are involved in a separate ongoing lawsuit over an order to stop government agencies using the company's AI tools. The BBC has approached the US Department of Commerce for comment.

Neural solvers for the Vehicle Routing Problem (VRP) have typically relied on either node or edge inputs, limiting their flexibility and generalization in real-world scenarios. We propose UniteFormer, a unified neural solver that supports node-only, edge-only, and hybrid input types through a single model trained via joint edge-node modalities. UniteFormer introduces: (1) a mixed encoder that integrates graph convolutional networks and attention mechanisms to collaboratively process node and edge features, capturing cross-modal interactions between them; and (2) a parallel decoder enhanced with query mapping and a feed-forward layer for improved representation. The model is trained with REINFORCE by randomly sampling input types across batches. Experiments on the Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP) demonstrate that UniteFormer achieves state-of-the-art performance and generalizes effectively to TSPLib and CVRPLib instances. These results underscore UniteFormer's ability to handle diverse input modalities and its strong potential to improve performance across various VRP tasks.

Adaptive learning, which requires the in-depth understanding of students' learning processes and rational planning of learning resources, plays a crucial role in intelligent education. However, how to effectively model these two processes and seamlessly integrate them poses significant implementation challenges for adaptive learning. As core learning resources, exercises have the potential to diagnose students' knowledge states during the learning processes and provide personalized learning recommendations to strengthen students' knowledge, thereby serving as a bridge to boost student-oriented adaptive learning. Therefore, we introduce a novel task called Knowledge-aware Exercise Generative Recommendation (KEGR). It aims to dynamically infer students' knowledge states from their past exercise responses and customizably generate new exercises. To achieve KEGR, we propose an adaptive multi-agent cooperation framework, called ExeGen, inspired by the excellent reasoning and generative capabilities of LLM-based AI agents. Specifically, ExeGen coordinates four specialized agents for supervision, knowledge state perception, exercise generation, and quality refinement through an adaptive loop workflow pipeline. More importantly, we devise two enhancement mechanisms in ExeGen: 1) A human-simulated knowledge perception mechanism mimics students' cognitive processes and generates interpretable knowledge state descriptions via demonstration-based In-Context Learning (ICL). In this mechanism, a dual-matching strategy is further designed to retrieve highly relevant demonstrations for reliable ICL reasoning.

Foundation models pretrained on web-scale data drive contemporary transfer learning in vision, language, and multimodal tasks. Recent work shows that mild label noise in these corpora may lift in-distribution accuracy yet sharply reduce out-of-distribution generalization, an effect known as catastrophic inheritance. Medical data is especially sensitive because annotations are scarce, domain shifts are large, and pretraining sources are noisy. We present the first systematic analysis of catastrophic inheritance in medical models. Controlled label-corruption experiments expose a clear structural collapse: as noise rises, the skewness and kurtosis of feature and logit distributions decline, signaling a flattened representation space and diminished discriminative detail. These higher-order statistics form a compact, interpretable marker of degradation in fine-grained tasks such as histopathology. Guided by this finding, we introduce a fine-tuning objective that restores skewness and kurtosis through two scalar regularizers added to the task loss. The method leaves the backbone unchanged and incurs negligible overhead. Tests on PLIP models trained with Twitter pathology images, as well as other large-scale vision and language backbones, show consistent gains in robustness and cross-domain accuracy under varied noise levels.

Anthropic Says It's Taking Claude Fable 5 Offline to Comply With US Government Order "The government believes it has become aware of a method of bypassing, or'jailbreaking' Fable 5," the company said in a blog post. Anthropic says it's disabling two AI models it launched earlier this week, Claude Fable 5 and Mythos 5, to comply with an export control directive it received Friday afternoon from the US government citing national security concerns. The unprecedented incident marks the latest flashpoint between Anthropic and the Trump administration . While the company says the order asked it to suspend access to "any foreign national, whether inside or outside the United States, including foreign national Anthropic employees," it has removed access for all of its customers to ensure compliance. Earlier this year, Trump's Department of Defense labeled Anthropic a " supply chain risk " after the Claude-maker sought to draw red lines over how the US military could use its technology.

The ability to continually learn new skills, retain, and flexibly deploy them to accomplish goals is a key feature of intelligent and efficient behavior. However, the neural mechanisms facilitating the continual learning and flexible (re-)composition of skills remain elusive. Here, we study continual learning and the compositional reuse of learned computations in recurrent neural network (RNN) models using a novel two-system approach: one system that infers'what' computation to perform, and one that implements'how' to perform it. We focus on a set of compositional cognitive tasks commonly studied in neuroscience. To construct the'what' system, we first show that a large family of tasks can be systematically described by a probabilistic generative model, where compositionality stems from a shared underlying vocabulary of discrete task-epochs.