Next week, five teens from Palestine will head to Panama to compete in one of the world's largest youth robotics competitions. To win--and then teach STEM to their peers displaced by the Israel-Hamas war. For the entirety of the past year, as the teenage roboticists of Team Palestine have been working on their latest project, their homeland has been engulfed in Israel's war with Hamas . Earlier this month, that all changed. With a fragile ceasefire in place, Israeli forces began to pull back from parts of Gaza, and the teens put the final touches on the project they hope will bring them victory: a robot that can maneuver through a series of simulated challenges based on the effects of climate change.

Republican John Reid used AI to create a mock debate with Democratic rival Ghazala Hashmi for the Virginia lieutenant governor's race after she declined real debates.

Online advertising platforms use automated auctions to connect advertisers with potential customers, requiring effective bidding strategies to maximize profits. Accurate ad impact estimation requires considering three key factors: delayed and long-term effects, cumulative ad impacts such as reinforcement or fatigue, and customer heterogeneity. However, these effects are often not jointly addressed in previous studies. To capture these factors, we model ad bidding as a Contextual Markov Decision Process (CMDP) with delayed Poisson rewards. For efficient estimation, we propose a two-stage maximum likelihood estimator combined with data-splitting strategies, ensuring controlled estimation error based on the first-stage estimator's (in)accuracy. Building on this, we design a reinforcement learning algorithm to derive efficient personalized bidding strategies. This approach achieves a near-optimal regret bound of $\tilde{O}{(dH^2\sqrt{T})}$, where $d$ is the contextual dimension, $H$ is the number of rounds, and $T$ is the number of customers. Our theoretical findings are validated by simulation experiments.

The worlds people have strong opinions about artificial intelligence (AI), and they want policymakers to listen. Governments are inviting public comment on AI, but as they translate input into policy, much of what citizens say is lost. Policymakers are missing a critical opportunity to build trust in AI and its governance. This paper compares three countries, Australia, Colombia, and the United States, that invited citizens to comment on AI risks and policies. Using a landscape analysis, the authors examined how each government solicited feedback and whether that input shaped governance. Yet in none of the three cases did citizens and policymakers establish a meaningful dialogue. Governments did little to attract diverse voices or publicize calls for comment, leaving most citizens unaware or unprepared to respond. In each nation, fewer than one percent of the population participated. Moreover, officials showed limited responsiveness to the feedback they received, failing to create an effective feedback loop. The study finds a persistent gap between the promise and practice of participatory AI governance. The authors conclude that current approaches are unlikely to build trust or legitimacy in AI because policymakers are not adequately listening or responding to public concerns. They offer eight recommendations: promote AI literacy; monitor public feedback; broaden outreach; hold regular online forums; use innovative engagement methods; include underrepresented groups; respond publicly to input; and make participation easier.

Large Language Model (LLM) conditioning refers to instructing an LLM to generate content in accordance with the norms and values of a specific culture, beliefs of a particular political orientation, or any desired text-specified semantic conditioning. Unfortunately, prompt engineering does not ensure that LLMs behave in accordance with a desired conditioning due to the inductive bias of the pre-training and alignment datasets. Prior works have focused on fine-tuning LLMs by directly conditioning the LoRA weights; however, such methods introduce a large number of parameters. As a remedy, we propose Zhyper, a parameter-efficient factorized hypernetwork framework that generates context-aware LoRA adapters from textual descriptions. Experiments on multiple benchmarks show that Zhyper achieves competitive performance with up to 26x fewer parameters than the state-of-the-art baselines. Furthermore, we extend Zhyper to cultural alignment, demonstrating improved generalization to out-of-domain settings and a better capturing of fine-grained contextual values. Large Language Models (LLMs) have transformed Natural Language Processing (NLP), Computer Vision (CV), and machine learning (ML) more broadly. They achieve state-of-the-art performance in text generation and comprehension across diverse domains, including code synthesis (Rozi ` ere et al., 2023), mathematical reasoning (Ahn et al., 2024), scientific writing (Geng et al., 2025; Eger et al., 2025), multimodal tasks such as text-image understanding and generation (Alayrac et al., 2022), and evaluation of machine translation and related tasks (Gu et al., 2025). This success stems from scaling to millions and billions of parameters. However, this scaling requires large computational resources, motivating the search for parameter-efficient fine-tuning (PEFT) techniques. Recent advances have made it possible to adapt LLMs to task-specific criteria, which is crucial for a broader applicability and acceptance of NLP systems. A recent stream of research leverages PEFT techniques (Ding et al., 2023; Weyssow et al., 2023; Prottasha et al., 2024), e.g., Low-Rank Adaptions (LoRA) (Hu et al., 2021) to adapt for desired task-specific values in an LLM. LoRA achieves this by freezing most of the pre-trained model's parameters and introducing trainable low-rank matrices, yielding weight correction terms. However, stand-alone LoRA approaches are primarily tailored for a single-task adaptation and may lose their effectiveness in a setting where an LLM needs to be adapted to various downstream settings.

WIRED recently demonstrated how to cheat at poker by hacking the Deckmate 2 card shufflers used in casinos. The mob was allegedly using the same trick to fleece victims for millions. Security researcher Joseph Tartaro demonstrates how he can insert a hacking device into a USB on the back of the shuffler that alters its code, then transmits the deck's order via Bluetooth to a phone app. The Deckmate 2 automatic card shufflers used in casinos, cardhouses, and high-end private poker games around the world are designed to shuffle a deck in seconds with perfect, computer-generated randomness, vastly speeding up play. They're also, amazingly, sold with a camera inside that can observe every card in the deck before it's dealt--a fact that's become very convenient for poker-cheating hackers and, allegedly, members of the Cosa Nostra mafia.

How much of Europe's oil still comes from Russia? Russia launched dozens of drones and decoy drones across Ukrainian territory, including one that hit a school building in Kyiv. Marco Rubio says implementing Gaza peace deal is'top priority' for Trump Body of'breadwinner' Thai captive held in Gaza returned home Displaced Palestinians forced to live in Gaza's graveyards

Ukraine's president has urged the European Union to back a plan to release billions of euros in frozen Russian cash to help fund the country's defence. As EU leaders met in Brussels, Volodymyr Zelensky said he hoped they would make a positive decision about using €140bn (£122bn) in Russian assets currently held in a Belgian clearing house. The controversial move would would be on top of sanctions the block has imposed on Russia - the latest on Thursday targeting the Kremlin's oil revenues. They followed US measures against Russia's oil industry earlier - the first time President Donald Trump has sanctioned Moscow as he grows frustrated over President Vladimir Putin's refusal to end the war. On Wednesday evening, the US president confirmed that a planned meeting with Putin in Budapest had been shelved indefinitely.

With the NYC mayoral race coming to a close, Andrew Cuomo is courting right-wing creators. With only 13 days left before the New York City mayoral election, former governor Andrew Cuomo is partnering with some of the same influencers who helped President Donald Trump win the White House last year. Over the past week, right-wing creators like Logan Paul, the former vlogger turned podcaster and WWE wrestler, and Emily Austin, an influencer and sports commentator, have published content featuring Cuomo as a guest on their shows. The appearances have marked a new investment by Cuomo's team into cultivating attention online as a means of competing against the social media-savvy Democratic nominee Zohran Mamdani . But instead of trying to cleave off Mamdani's online support, Cuomo appears to be trying to siphon off support from GOP nominee Curtis Sliwa.

How much of Europe's oil still comes from Russia? A Russian drone has killed two Ukrainian journalists and wounded another in the eastern Ukrainian city of Kramatorsk, according to their outlet and the regional governor of the Donetsk region. Freedom Media, a state-funded news organisation, said on Thursday that Olena Gramova, 43, and Yevgen Karmazin, 33, had been killed by a Russian Lancet drone while in their car at a petrol station in the industrial city. Another reporter, Alexander Kolychev, was hospitalised after the attack. Freedom Media said that Gramova, a native of Yenakiieve in the Donetsk region, had originally trained as a "finance specialist", but turned to journalism in 2014, the year when Russia annexed Ukraine's Crimean peninsula, and started arming a separatist movement in Donetsk and Luhansk in the Donbas.