Romania and its NATO allies have reacted angrily after a Russian drone crashed into an apartment building in eastern Romania, injuring two people. The Ministry of Foreign Affairs in Bucharest on Friday labelled the crash of the drone, part of an overnight attack aimed at Ukraine, a serious violation of international law. The incident is just the latest incursion along the alliance's eastern flank, raising concern that the risk of an open confrontation between Russia and NATO states is rising. Romania said the overnight drone was tracked by radar in its airspace before crashing onto the roof of a residential building in the city of Galati. Two F-16 fighter jets and a helicopter were scrambled, as authorities issued emergency alerts to residents.

Romania says a drone struck an apartment building in Galați, injuring a woman and child, marking the first time a Russian drone hit a populated area in the NATO member state.

A Russian drone hit an apartment building in Romania, the country's defence ministry said early on Friday, causing a fire and injuring two people. The drone crashed in the eastern city of Galati as Russia carried out attacks in Ukraine near the border, the ministry said in a statement. The Romanian General Inspectorate for Emergency Situations said the drone's entire explosive payload detonated, causing a fire on the 10th floor of the residential building. Russian drones have strayed across the border of the Nato member country a number of times during the four-year war with Ukraine, but this was the first time citizens from Romania had been hurt. Russia has yet to comment on the incident. This incident represents a serious and irresponsible escalation on the part of the Russian Federation, Romania's foreign ministry said, adding Bucharest had informed the Nato secretary general and requested measures to accelerate the transfer of anti-drone capabilities to Romania.

Anthropic has usurped OpenAI as the world's most valuable artificial intelligence startup, soaring to a $965bn valuation ahead of expected public listings by the rival firms. Anthropic, the maker of the Claude family of chatbots, said on Thursday that it had raised $65bn from private investors after a fundraising round led by Altimeter Capital, Greenoaks, Dragoneer and Sequoia Capital. "This funding will help us serve the historic demand we are experiencing, stay at the research frontier, and bring Claude to more of the places where work happens," Anthropic's Chief Financial Officer Krishna Rao said in a statement. Altimeter Capital CEO Brad Gerstner hailed the adoption of Claude among the "world's most demanding organisations" as evidence of Anthropic's command in the field. "This momentum positions Anthropic to lead the next phase of AI innovation and capture the enormous opportunity ahead," Gerstner said.

Taiyo Yuden sees'scary' levels of AI parts demand risking supply chain Multilayer ceramic capacitors, which are tiny components that regulate and stabilize power flow in electronic devices, are becoming a growing bottleneck in the construction of artificial intelligence data centers. Taiyo Yuden is fielding "scary" levels of demand for its high-end artificial intelligence server components, stretching capacity and increasing the risk of supply chain hiccups. The Tokyo-based company, which makes multilayer ceramic capacitors, will likely need to accelerate spending to expand output capacity, Chief Executive Officer Katsuya Sase said in an interview. MLCCs, which are tiny components that regulate and stabilize power flow in electronic devices, are becoming a growing bottleneck in the construction of artificial intelligence data centers. Taiyo Yuden and Murata Manufacturing comprise the bulk of the world's supplies of high-end MLCCs. "The volumes we are seeing today -- it's scary," Sase said.

BYD debuts China's most advanced EV chip in smart-driving push BYD on Thursday unveiled what it calls China's first automotive-grade 4-nanometer chip for self-driving cars. BYD, the world's largest electric vehicle maker, unveiled a series of technology advances, including what it calls China's first automotive-grade 4-nanometer chip for self-driving cars. The semiconductor breakthrough approaches the lead of Chinese tech giant Huawei Technologies, which currently makes chips with a geometry of 7 nm but has pledged to debut 1.4 nm chips by 2031. It's designed to allow BYD's computer-assisted driving to stand out from a crowded Chinese EV market that includes rivals such as Xpeng and Xiaomi. Facing eight months in a row of falling sales and intense competition for more advanced charging and intelligent driving technologies, BYD is looking to spark more demand for its vehicles.

Anthropic's rise came by doubling down on delivering generative artificial intelligence to enterprise clients rather than general users. Artificial intelligence company Anthropic said Thursday it had raised $65 billion in a new funding round that values the Claude maker at $965 billion, more than its archrival OpenAI, the maker of ChatGPT. The latest fundraising round confirms Anthropic's place as one of the most significant players in AI, with the startup led by Dario Amodei having drawn fans for its coding powers and state-of-the-art models. Anthropic's rise came by doubling down on delivering generative AI to enterprise clients rather than general users, the path initially chosen by OpenAI. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Model-based reinforcement learning (MBRL) agents typically learn world models by minimizing predictive loss. However, powerful RL optimizers inevitably exploit minor model inaccuracies, leading to simulator exploitation and a reality gap where policies succeed in simulation but fail in the real world. We propose that the objective for learning simulators should be strategic robustness rather than predictive accuracy, and formulate this as a zero-sum minimax game between a model player and an adversarial policy player. We provide a comprehensive theoretical analysis: (1) an online learning guarantee showing the game is learnable with sublinear regret bounds; (2) a tractable critic-based simplification bounding the global policy-value gap by the local critic's loss; and (3) an Error-MDP duality, proving that finding the worst-case policy is formally dual to a standard RL problem where the reward is the one-step critic error. This duality yields a provably convergent active data selection algorithm. Experiments on continuous control tasks demonstrate that our approach reduces prediction error in strategically important regions by $1.5$-$2.2\times$ and enables policies trained purely in simulation to match near-optimal real-world performance.

Conformal prediction (CP) and its extension, conformal risk control (CRC), are established frameworks for quantifying uncertainty in supervised machine learning through formal guarantees. However, recent breakthroughs in artificial intelligence (AI) have been driven by unsupervised generative models, such as large language models (LLMs) and image generators, which are not directly compatible with CP or CRC. In this work we introduce conformal generation (Conf-Gen), a general framework adapting CRC to generative tasks while relaxing its theoretical assumptions. Conf-Gen unifies and generalizes previous attempts to apply CP to LLMs, and extends conformal methodology to entirely new domains. We demonstrate the flexibility of Conf-Gen through some novel applications, including obtaining conformal guarantees on: image generators producing non-memorized images, conversational AI systems having asked enough clarifying questions, and the output of AI agents being correct.

Federated Conformal RAG (FC-RAG) provides distribution-free coverage for a bandwidth-limited swarm of weak language models, but only at a fixed horizon. We extend it to anytime-valid sequential coverage: validity at every stopping time, preserved under predictable adaptive control (recalibration, per-node bandwidth escalation, distilled-student refresh), at no extra cost in assumptions over fixed-horizon FC-RAG. Naive composition fails because FC-RAG's marginal coverage bound makes the betting e-process a non-supermartingale on adverse calibration draws, and Ville's inequality cannot be invoked. We give Anytime-FC-RAG, a sequential extension built on a summable per-step calibration-deviation budget that converts the marginal bound into a strict conditional bound on a calibration-good event, paired with a truncated betting e-process that is a nonnegative supermartingale on the entire probability space. From these two ingredients, we obtain four guarantees: time-uniform alarm validity $\mathbb{P}(\sup_t E_t \ge 1/δ_e) \le δ_e + δ_{\mathrm{cal}}$, a Hoeffding-stitched cumulative-miscoverage envelope at the same total budget, safety under any predictable controller (recalibration, bandwidth escalation, student refresh), and training-side error propagation across an unbounded sequence of Federated Probe-Logit Distillation (FPLD) refreshes via a summable training budget. As a practical consequence, an adaptive controller that escalates retrieval bandwidth only when the e-process crosses a warning threshold matches the alarm rate of a fixed-high-bandwidth schedule at substantially lower communication cost. Experiments on a GPT-2-small + MiniLM swarm across MMLU, DBpedia, and AG News verify the predicted alarm rate, detection delay, envelope coverage, and $14$-$57\%$ bandwidth savings; the alarm fires when and only when coverage genuinely breaks.