Plus: AI reportedly caused ICE to send agents into the field without training, Palantir's app for targeting immigrants gets exposed, and more. As Immigration and Customs Enforcement continues its "Operation Metro Surge" infiltration of Minnesota, more than 2,000 ICE operatives and about 1,000 other federal agents have made more than 2,400 arrests since the operation began in late 2025, and tear gassed protesters. Last week, an ICE agent shot and killed local resident Renee Nicole Good, a 37-year-old US citizen. In response, the state of Minnesota and the Twin Cities' local governments sued the US government and several officials this week to stop the operation . WIRED reported on a contract justification published in a federal register on Tuesday that says 31 ICE vehicles currently operating in Minnesota "lack the necessary emergency lights and sirens" to be "compliant" with regulations.

Large-scale traffic forecasting relies on fixed sensor networks that often exhibit blackouts: contiguous intervals of missing measurements caused by detector or communication failures. These outages are typically handled under a Missing At Random (MAR) assumption, even though blackout events may correlate with unobserved traffic conditions (e.g., congestion or anomalous flow), motivating a Missing Not At Random (MNAR) treatment. We propose a latent state-space framework that jointly models (i) traffic dynamics via a linear dynamical system and (ii) sensor dropout via a Bernoulli observation channel whose probability depends on the latent traffic state. Inference uses an Extended Kalman Filter with Rauch-Tung-Striebel smoothing, and parameters are learned via an approximate EM procedure with a dedicated update for detector-specific missingness parameters. On the Seattle inductive loop detector data, introducing latent dynamics yields large gains over naive baselines, reducing blackout imputation RMSE from 7.02 (LOCF) and 5.02 (linear interpolation + seasonal naive) to 4.23 (MAR LDS), corresponding to about a 64% reduction in MSE relative to LOCF. Explicit MNAR modeling provides a consistent but smaller additional improvement on real data (imputation RMSE 4.20; 0.8% RMSE reduction relative to MAR), with similar modest gains for short-horizon post-blackout forecasts (evaluated at 1, 3, and 6 steps). In controlled synthetic experiments, the MNAR advantage increases as the true missingness dependence on latent state strengthens. Overall, temporal dynamics dominate performance, while MNAR modeling offers a principled refinement that becomes most valuable when missingness is genuinely informative.

A blackout in San Francisco revealed a new way for robotaxis to go wrong. Waymo's self-driving robotaxis can successfully nail a tricky left turn, weave through lanes to drop you off at the airport, and safely pass a U-Haul that's idling in the middle of the street. But during a blackout, they apparently turn into four-wheel bricks. On Saturday, when a major power outage in San Francisco knocked out traffic signals, many Waymo vehicles didn't pull over to the side of the road or seek out a parking space. Nor did they treat intersections as four-way stops, as a human would have. Instead, they just sat there with their hazard lights on, like a student driver freezing up before their big parallel-parking test.

A widespread power failure plunged San Francisco into darkness on Saturday night, disrupting traffic citywide and forcing numerous self-driving Waymo taxis to stop abruptly in the middle of streets and intersections. As electricity went out across large portions of the city, traffic signals failed, leaving autonomous vehicles unable to operate as normal. Photos and videos shared by users on X showed Waymo robotaxis frozen in place, backing up traffic and creating hazardous conditions for other drivers. Waymo confirmed on Saturday evening that it had shut down its driverless ride-hailing service throughout San Francisco after footage circulated online showing its vehicles blocking roads during the blackout. "We have temporarily suspended our ride-hailing services in the San Francisco Bay Area due to the widespread power outage," Waymo spokesperson Suzanne Philion said in a statement to several news outlets.

Dominican Republic suffers nationwide power cut after'cascade of failures' The Dominican Republic has experienced a nationwide power cut which officials said was linked to a failure in the electricity transmission system. At 13:23 local time (17:23 GMT) an issue at a substation caused a nationwide interruption to power services, the state-owned Dominican Electricity Transmission Company said, citing the country's energy minister Joel Santos Echeverría. Echeverría said a thorough investigation would be carried out to identify the cause and that work was under way to quickly restore power. The Caribbean nation, which is home to around 11 million people, has been experiencing smaller blackouts in recent weeks, the AFP news agency reports. Officials at the state-owned power company said generation units in two major power plants had shut down, causing a cascade of failures in other parts of the grid.

Blackouts hit Russia's Belgorod as Ukrainian drone attacks surge Residents of Russia's Belgorod region say blackouts, air-raid sirens and the sound of gunfire aimed at incoming Ukrainian drones are becoming increasingly common, as Kyiv retaliates against repeated bombardments of its cities with cross-border strikes of its own. It's so loud and so terrifying, says Nina, a Belgorod resident who asked us to change her name. I was coming back from the clinic when a siren went off. As usual, I received Telegram alerts about a drone attack. Then bursts of automatic gunfire broke out, I ran into a nearby courtyard and tried to hide under an arch, she recalls.

Millions of Americans may soon face nearly a full month of power blackouts each year, disrupting daily life, businesses, and critical services across the country. White House officials warned on Monday that the retiring power plants and soaring electricity demand could push the US grid to its limits, triggering over 800 hours of power outages annually. From hospitals to data centers, the ripple effects of extended blackouts could impact nearly every part of daily life for US residents. Department of Energy (DOE) Secretary Chris Wright said: 'In the coming years, America's reindustrialization and the AI race will require a significantly larger supply of around-the-clock, reliable, and uninterrupted power. 'President Trump's administration is committed to advancing a strategy of energy addition, and supporting all forms of energy that are affordable, reliable, and secure.'

Accurately modeling consumer behavior in energy operations remains challenging due to inherent uncertainties, behavioral complexities, and limited empirical data. This paper introduces a novel approach leveraging generative agents--artificial agents powered by large language models--to realistically simulate customer decision-making in dynamic energy operations. We demonstrate that these agents behave more optimally and rationally in simpler market scenarios, while their performance becomes more variable and suboptimal as task complexity rises. Furthermore, the agents exhibit heterogeneous customer preferences, consistently maintaining distinct, persona-driven reasoning patterns. Our findings highlight the potential value of integrating generative agents into energy management simulations to improve the design and effectiveness of energy policies and incentive programs.

Missing values in multivariate time series data can harm machine learning performance and introduce bias. These gaps arise from sensor malfunctions, blackouts, and human error and are typically addressed by data imputation. Previous work has tackled the imputation of missing data in random, complete blackouts and forecasting scenarios. The current paper addresses a more general missing pattern, which we call "partial blackout," where a subset of features is missing for consecutive time steps. We introduce a two-stage imputation process using self-attention and diffusion processes to model feature and temporal correlations. Notably, our model effectively handles missing data during training, enhancing adaptability and ensuring reliable imputation and performance, even with incomplete datasets. Our experiments on benchmark and two real-world time series datasets demonstrate that our model outperforms the state-of-the-art in partial blackout scenarios and shows better scalability.

Anomaly detection is a critical requirement for ensuring safety in autonomous driving. In this work, we leverage Cooperative Perception to share information across nearby vehicles, enabling more accurate identification and consensus of anomalous behaviors in complex traffic scenarios. To account for the real-world challenge of imperfect communication, we propose a cooperative-perception-based anomaly detection framework (CPAD), which is a robust architecture that remains effective under communication interruptions, thereby facilitating reliable performance even in low-bandwidth settings. Since no multi-agent anomaly detection dataset exists for vehicle trajectories, we introduce 15,000 different scenarios with a 90,000 trajectories benchmark dataset generated through rule-based vehicle dynamics analysis. Empirical results demonstrate that our approach outperforms standard anomaly classification methods in F1-score, AUC and showcase strong robustness to agent connection interruptions.