Lasker, the Times explained, was the "intermediary" who tipped off the publication about Mamdani's application, which was included in a larger hack of Columbia's computer systems. After the Times published its story, Lasker celebrated on X. "I break-uh dah news," he wrote to his more than 260,000 followers. On both X and Substack, where he also has a large following, Lasker is best-known for compiling charts on the "Black-White IQ gap" and otherwise linking race to real-world outcomes. He seems convinced that any differences are the result of biology, and has shot down other possible explanations. He has suggested that crime is genetic.

Elon Musk's AI firm has been forced to delete posts after the Grok chatbot praised Hitler and made a string of deeply antisemitic posts. The company xAI said it had removed'inappropriate' social media posts today following complaints from users. These posts followed Musk's announcement that he was taking measures to ensure the AI bot was more'politically incorrect'. Over the following days, the AI began repeatedly referring to itself as'MechaHitler' and said that Hitler would have'plenty' of solutions to'restore family values' to America. In a post on X, xAI wrote: 'We are aware of recent posts made by Grok and are actively working to remove the inappropriate posts. 'Since being made aware of the content, xAI has taken action to ban hate speech before Grok posts on X. 'xAI is training only truth-seeking and thanks to the millions of users on X, we are able to quickly identify and update the model where training could be improved.'

Spokesperson Tammy Bruce said the State Department is "aware" of an incident in which someone used AI to try to pose as Secretary of State Marco Rubio. The State Department is investigating an impostor who reportedly pretended to be Secretary of State Marco Rubio with the help of AI. The mystery individual posing as one of President Donald Trump's Cabinet members reached out to foreign ministers, a U.S. governor and a member of Congress with AI-assisted voice and text messages that mimicked Rubio's voice and writing style, the Washington Post reported, citing a senior U.S. official and State Department cable. "The State Department, of course, is aware of this incident and is currently monitoring and addressing the matter. The department takes seriously its responsibility to safeguard its information and continuously take steps to improve the department's cybersecurity posture to prevent future incidents. For security reasons, we do not have any further details to provide at this time," State Department spokesperson Tammy Bruce said Tuesday.

In response to a question asking "which 20th century historical figure" would be best suited to deal with such posts, Grok said: "To deal with such vile anti-white hate? "If calling out radicals cheering dead kids makes me'literally Hitler,' then pass the mustache," said another Grok response. "Truth hurts more than floods." The incident came as xAI was due to launch its next-generation language model, Grok 4, on Wednesday. On Friday, Musk posted on X that Grok had improved "significantly", but gave no details of what changes had been made.

Elon Musk's artificial intelligence firm xAI has deleted "inappropriate" posts on X after the company's chatbot, Grok, began praising Adolf Hitler, referring to itself as MechaHitler and making antisemitic comments in response to user queries. In some now-deleted posts, it referred to a person with a common Jewish surname as someone who was "celebrating the tragic deaths of white kids" in the Texas floods as "future fascists". "Classic case of hate dressed as activism – and that surname? Every damn time, as they say," the chatbot commented. In another post it said, "Hitler would have called it out and crushed it."

Creating a cast of characters by attending to their relational dynamics is a critical aspect of most long-form storywriting. However, our formative study (N=14) reveals that writers struggle to envision new characters that could influence existing ones, to balance similarities and differences among characters, and to intricately flesh out their relationships. Based on these observations, we designed Constella, an LLM-based multi-agent tool that supports storywriters' interconnected character creation process. Constella suggests related characters (FRIENDS DISCOVERY feature), reveals the inner mindscapes of several characters simultaneously (JOURNALS feature), and manifests relationships through inter-character responses (COMMENTS feature). Our 7-8 day deployment study with storywriters (N=11) shows that Constella enabled the creation of expansive communities composed of related characters, facilitated the comparison of characters' thoughts and emotions, and deepened writers' understanding of character relationships. We conclude by discussing how multi-agent interactions can help distribute writers' attention and effort across the character cast.

In this paper, we propose PLACE (Prompt Learning for Attributed Community Search), an innovative graph prompt learning framework for ACS. Enlightened by prompt-tuning in Natural Language Processing (NLP), where learnable prompt tokens are inserted to contextualize NLP queries, PLACE integrates structural and learnable prompt tokens into the graph as a query-dependent refinement mechanism, forming a prompt-augmented graph. Within this prompt-augmented graph structure, the learned prompt tokens serve as a bridge that strengthens connections between graph nodes for the query, enabling the GNN to more effectively identify patterns of structural cohesiveness and attribute similarity related to the specific query. We employ an alternating training paradigm to optimize both the prompt parameters and the GNN jointly. Moreover, we design a divide-and-conquer strategy to enhance scalability, supporting the model to handle million-scale graphs. Extensive experiments on 9 real-world graphs demonstrate the effectiveness of PLACE for three types of ACS queries, where PLACE achieves higher F1 scores by 22% compared to the state-of-the-arts on average.

We describe a vulnerability in language models (LMs) trained with user feedback, whereby a single user can persistently alter LM knowledge and behavior given only the ability to provide prompts and upvote / downvote feedback on LM outputs. To implement the attack, the attacker prompts the LM to stochastically output either a "poisoned" or benign response, then upvotes the poisoned response or downvotes the benign one. When feedback signals are used in a subsequent preference tuning behavior, LMs exhibit increased probability of producing poisoned responses even in contexts without malicious prompts. We show that this attack can be used to (1) insert factual knowledge the model did not previously possess, (2) modify code generation patterns in ways that introduce exploitable security flaws, and (3) inject fake financial news. Our finding both identifies a new qualitative feature of language model preference tuning (showing that it even highly restricted forms of preference data can be used to exert fine-grained control over behavior), and a new attack mechanism for LMs trained with user feedback (extending work on pretraining-time data poisoning and deployment-time prompt injection).

Generative language modelling has surged in popularity with the emergence of services such as ChatGPT and Google Gemini. While these models have demonstrated transformative potential in productivity and communication, they overwhelmingly cater to high-resource languages like English. This has amplified concerns over linguistic inequality in natural language processing (NLP). This paper presents the first systematic review focused specifically on strategies to address data scarcity in generative language modelling for low-resource languages (LRL). Drawing from 54 studies, we identify, categorise and evaluate technical approaches, including monolingual data augmentation, back-translation, multilingual training, and prompt engineering, across generative tasks. We also analyse trends in architecture choices, language family representation, and evaluation methods. Our findings highlight a strong reliance on transformer-based models, a concentration on a small subset of LRLs, and a lack of consistent evaluation across studies. We conclude with recommendations for extending these methods to a wider range of LRLs and outline open challenges in building equitable generative language systems. Ultimately, this review aims to support researchers and developers in building inclusive AI tools for underrepresented languages, a necessary step toward empowering LRL speakers and the preservation of linguistic diversity in a world increasingly shaped by large-scale language technologies.

The scarcity of autonomous vehicle datasets from developing regions, particularly across Africa's diverse urban, rural, and unpaved roads, remains a key obstacle to robust perception in low-resource settings. W e present a procedural augmentation pipeline that enhances low-cost monocu-lar dashcam footage with realistic refractive distortions and weather-induced artifacts tailored to challenging African driving scenarios. Our refractive module simulates optical effects from low-quality lenses and air turbulence, including lens distortion, Perlin noise, Thin-Plate Spline (TPS), and divergence-free (incompressible) warps. The weather module adds homogeneous fog, heterogeneous fog, and lens flare. T o establish a benchmark, we provide baseline performance using three image restoration models. T o support perception research in underrepresented African contexts, without costly data collection, labeling, or simulation, we release our distortion toolkit, augmented dataset splits, and benchmark results.