The Download: how AI is used for military targeting, and the Pentagon's war on Claude Plus: an ex-DOGE staffer has been accused of stealing social security data. The US military might use generative AI systems to rank targets and recommend which to strike first, according to a Defense Department official. A list of possible targets could first be fed into a generative AI system that the Pentagon is fielding for classified settings. Humans might then ask the system to analyze the information and prioritize the targets. They would then be responsible for checking and evaluating the results and recommendations. OpenAI's ChatGPT and xAI's Grok could soon be at the center of exactly these sorts of high-stakes military decisions.

Police say at least 25 kids used e-bikes and scooters to evade arrest and intimidate drivers. Breakthroughs, discoveries, and DIY tips sent six days a week. Australian police are cracking down on groups of unruly teenagers who they say are using deceptively speedy e-bikes and scooters to engage in "antisocial riding behavior." Their solution: confiscate the popular micromobility devices and crush them. The roundup, dubbed Operation Moorhead, began last week in the suburbs of Perth in southwestern Australia.

Large Language Models (LLMs) are commonly evaluated for robustness against paraphrased or semantically equivalent jailbreak prompts, yet little attention has been paid to linguistic variation as an attack surface. In this work, we systematically study how linguistic styles such as fear or curiosity can reframe harmful intent and elicit unsafe responses from aligned models. We construct style-augmented jailbreak benchmark by transforming prompts from 3 standard datasets into 11 distinct linguistic styles using handcrafted templates and LLM-based rewrites, while preserving semantic intent. Evaluating 16 open- and close-source instruction-tuned models, we find that stylistic reframing increases jailbreak success rates by up to +57 percentage points. Styles such as fearful, curious and compassionate are most effective and contextualized rewrites outperform templated variants. To mitigate this, we introduce a style neutralization preprocessing step using a secondary LLM to strip manipulative stylistic cues from user inputs, significantly reducing jailbreak success rates. Our findings reveal a systemic and scaling-resistant vulnerability overlooked in current safety pipelines.

Modeling the dynamics of micro-mobility vehicles (MMV) is becoming increasingly important for training autonomous vehicle systems and building urban traffic simulations. However, mainstream tools rely on variants of the Kinematic Bicycle Model (KBM) or mode-specific physics that miss tire slip, load transfer, and rider/vehicle lean. To our knowledge, no unified, physics-based model captures these dynamics across the full range of common MMVs and wheel layouts. We propose the "Generalized Micro-mobility Model" (GM3), a tire-level formulation based on the tire brush representation that supports arbitrary wheel configurations, including single/double track and multi-wheel platforms. We introduce an interactive model-agnostic simulation framework that decouples vehicle/layout specification from dynamics to compare the GM3 with the KBM and other models, consisting of fixed step RK4 integration, human-in-the-loop and scripted control, real-time trajectory traces and logging for analysis. We also empirically validate the GM3 on the Stanford Drone Dataset's deathCircle (roundabout) scene for biker, skater, and cart classes.

A few EV makers in India went belly up after the government abruptly scaled back incentives and cracked down on the misuse of subsidies. Ather survived the storm and sales are increasing. More than 70% of the 200 million registered vehicles in India are two-wheelers. Ather Energy builds e-scooters for the rising middle class that could help commuters ditch highly-polluting, gas-guzzling models. While sales of Tesla or BYD cars drove electric vehicle adoption elsewhere in the world, two-wheelers have led the green energy transition in India. As one of the earliest "pure play" e-scooter makers, Ather Energy has helped drive micromobility EV penetration throughout India and boosted the shift away from carbon-emitting vehicles.

California passes a new law aimed at shining a light on the growing number of crashes involving self-driving cars. Chances are, you have never actually ridden a scooter like this, zipping around corners, but you have definitely seen them weaving through city traffic. Just when you thought scooters were already a wild card on the road, imagine one that drives itself. That is exactly what the Omoway Omo X promises. Developed by a team of former Xpeng engineers, this scooter is not just electric, it is packed with smart features that push self-driving scooter tech to a whole new level, offering far more than you would ever expect from a two-wheeler.

The rapid adoption of micromobility solutions, particularly two-wheeled vehicles like e-scooters and e-bikes, has created an urgent need for reliable autonomous riding (AR) technologies. While autonomous driving (AD) systems have matured significantly, AR presents unique challenges due to the inherent instability of two-wheeled platforms, limited size, limited power, and unpredictable environments, which pose very serious concerns about road users' safety. This review provides a comprehensive analysis of AR systems by systematically examining their core components, perception, planning, and control, through the lens of AD technologies. We identify critical gaps in current AR research, including a lack of comprehensive perception systems for various AR tasks, limited industry and government support for such developments, and insufficient attention from the research community. The review analyses the gaps of AR from the perspective of AD to highlight promising research directions, such as multimodal sensor techniques for lightweight platforms and edge deep learning architectures. By synthesising insights from AD research with the specific requirements of AR, this review aims to accelerate the development of safe, efficient, and scalable autonomous riding systems for future urban mobility.

For future human-autonomous vehicle (AV) interactions to be effective and smooth, human-aware systems that analyze and align human needs with automation decisions are essential. Achieving this requires systems that account for human cognitive states. We present a novel computational model in the form of a Dynamic Bayesian Network (DBN) that infers the cognitive states of both AV users and other road users, integrating this information into the AV's decision-making process. Specifically, our model captures the well-being of both an AV user and an interacting road user as cognitive states alongside trust. Our DBN models infer beliefs over the AV user's evolving well-being, trust, and intention states, as well as the possible well-being of other road users, based on observed interaction experiences. Using data collected from an interaction study, we refine the model parameters and empirically assess its performance. Finally, we extend our model into a causal inference model (CIM) framework for AV decision-making, enabling the AV to enhance user well-being and trust while balancing these factors with its own operational costs and the well-being of interacting road users. Our evaluation demonstrates the model's effectiveness in accurately predicting user's states and guiding informed, human-centered AV decisions.

As Large language models have shown a remarkable a significant milestone in this area, Elhage et al. ability to learn and perform complex tasks through (2021) demonstrated the existence of induction in-context learning (ICL) (Brown et al., 2020; Touvron heads in Transformer LMs. These heads scan the et al., 2023b). In ICL, the model receives context for previous instances of the current token a demonstration context and a query question as using a prefix matching mechanism, which identifies a prompt for prediction. Unlike supervised learning, if and where a token has appeared before. ICL utilises the pretrained model's capabilities If a matching token is found, the head employs to recognise and replicate patterns within the a copying mechanism to increase the probability demonstration context, thereby enabling accurate of the subsequent token, facilitating exact or approximate predictions for the query without the use of gradient repetition of sequences and embodying updates.

On the morning of April 3, Taiwan was hit by a 7.4 magnitude earthquake. Seconds later, hundreds of battery-swap stations in Taiwan sensed something else: the power frequency of the electric grid took a sudden drop, a signal that some power plants had been disconnected in the disaster. The grid was now struggling to meet energy demand. These stations, built by the Taiwanese company Gogoro for electric-powered two-wheeled vehicles like scooters, mopeds, and bikes, reacted immediately. According to numbers provided by the company, 590 Gogoro battery-swap locations (some of which have more than one swap station) stopped drawing electricity from the grid, lowering local demand by a total six megawatts--enough to power thousands of homes. It took 12 minutes for the grid to recover, and the battery-swap stations then resumed normal operation.