Hiroo Ota (center left), CEO of Kioxia Holdings, and others unveil Kioxia's new 3D flash memory chip at its Kitakami plant in Kitakami, Iwate Prefecture, on Friday. Kioxia Holdings has started shipping samples of its next-generation flash memory chips to artificial-intelligence data center operators, seeking to gain ground in the lucrative business against rivals. The Tokyo-based chipmaker's latest high-density 3D flash memory chips aim to better meet AI data center needs with better efficiency and transmission speeds. The 332-layer 10th-generation chips pack more data into silicon and can store 59% more data compared with its previous flagship 8th-generation chip, the company said Friday. Production will take place at the company's second manufacturing facility at its Kitakami plant in Iwate Prefecture, which began operating in September last year.

U.S. defense firm Anduril in talks for Nissan plant to build drones in Japan, sources say U.S. defense firm Anduril Industries is in talks to acquire the plant to build military drones in Japan, sources say. U.S. defense firm Anduril Industries is in talks to acquire Nissan Motor's Oppama assembly plant near Tokyo as the maker of autonomous weapons looks to build military drones in Japan, according to three sources familiar with the matter. While they say no decision has been made, any deal could transform one of Japan's first large-scale postwar car factories, long a symbol of its industrial revival, into an arms-making hub. The talks over Oppama, which are being reported for the first time, come as Prime Minister Sanae Takaichi's government seeks to expand defense manufacturing amid growing concern that a Taiwan Strait crisis could draw in Japan and run down weapons stocks. 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.

Where does'A man is walking in a Locate the moment where "A man For the query'A man recommends narrow alley, with street noise and Determine the precise timestamp in wearing a white mask is speaking visiting local areas in Tokyo, filming the conversations in the background.

A penguin is standing on the lawn, with a giraffe behind it. A young man stands in front of the Statue of Liberty. A man in a tuxedo stands beside Tokyo Tower. A man is standing next to a traditional Japanese lantern. A woman is looking at a small, fluffy dog.

We study the reinforcement learning (RL) problem in a constrained Markov decision process (CMDP), where an agent explores the environment to maximize the expected cumulative reward while satisfying a single constraint on the expected total utility value in every episode. While this problem is well understood in the tabular setting, theoretical results for function approximation remain scarce. This paper closes the gap by proposing an RL algorithm for linear CMDPs that achieves eO( K) regret with an episode-wise zero-violation guarantee. Furthermore, our method is computationally efficient, scaling polynomially with problem-dependent parameters while remaining independent of the state space size. Our results significantly improve upon recent linear CMDP algorithms, which either violate the constraint or incur exponential computational costs.

Recent advances in large language models (LLMs) have yielded impressive performance on various tasks, yet they often depend on high-quality feedback that can be costly. Self-refinement methods attempt to leverage LLMs' internal evaluation mechanisms with minimal human supervision; however, these approaches frequently suffer from inherent biases and overconfidence, especially in domains where the models lack sufficient internal knowledge, resulting in performance degradation. As an initial step toward enhancing self-refinement for broader applications, we introduce an iterative refinement pipeline that employs the Unlabeled-Unlabeled learning framework to improve LLM-generated pseudo-labels for classification tasks.

Online structured prediction is a task of sequentially predicting outputs with complex structures based on inputs and past observations, encompassing online classification. Recent studies showed that in the full-information setting, we can achieve finite bounds on the surrogate regret, i.e., the extra target loss relative to the best possible surrogate loss. In practice, however, full-information feedback is often unrealistic as it requires immediate access to the whole structure of complex outputs. Motivated by this, we propose algorithms that work with less demanding feedback, bandit and delayed feedback. For bandit feedback, by using a standard inverseweighted gradient estimator, we achieve a surrogate regret bound of O( KT) for the time horizon T and the size of the output set K. However, K can be extremely large when outputs are highly complex, resulting in an undesirable bound. To address this issue, we propose another algorithm that achieves a surrogate regret bound of O(T2/3), which is independent of K. This is achieved with a carefully designed pseudo-inverse matrix estimator. Furthermore, we numerically compare the performance of these algorithms, as well as existing ones. Regarding delayed feedback, we provide algorithms and regret analyses that cover various scenarios, including full-information and bandit feedback, as well as fixed and variable delays.

We present The Matrix, a foundational realistic world simulator capable of generating infinitely long 720p high-fidelity real-scene video streams with real-time, responsive control in both first-and third-person perspectives. Trained on limited supervised data from video games like Forza Horizon 5 and Cyberpunk 2077, complemented by large-scale unsupervised footage from real-world settings like Tokyo streets, The Matrix allows users to traverse diverse terrains--deserts, grasslands, water bodies, and urban landscapes--in continuous, uncut hour-long sequences. With speeds of up to 16 FPS, the system supports real-time interactivity and demonstrates zero-shot generalization, translating virtual game environments to real-world contexts where collecting continuous movement data is often infeasible. For example, The Matrix can simulate a BMW X3 driving through an office setting--an environment present in neither gaming data nor real-world sources. This approach showcases the potential of game data to advance robust world models, bridging the gap between simulations and real-world applications in scenarios with limited data.

A group of high school students arrested over allegedly trying to extort money from a boy in western Tokyo may have used ChatGPT to decide how much to demand, police said. A group of high school students in Tokyo arrested over allegedly assaulting a boy and trying to extort money from him may have used ChatGPT to decide how much to demand, media reports have recently revealed. Five teenagers, including a 17-year-old girl and four boys ranging in age from 16 to 17, were arrested in January over the alleged assault and attempted extortion of a 17-year-old high school student in the city of Hachioji in western Tokyo, according to the Metropolitan Police Department. Police said the suspects assaulted the boy in a plaza in Hachioji's Shiroyamate district, breaking his nose and causing other injuries, before allegedly trying to extort ¥150,000 ($935) from him. The girl, who was the victim's ex-girlfriend, allegedly first confronted him, accusing him of touching her younger sister's leg. She then challenged him, saying, "Give me the money or fight me one-on-one," according to reports by Fuji TV.

Dipu Tamang from Nepal is among more than 400,000 international students in Japan. When Dipu Tamang arrived in Japan from Nepal in 2024, he joined a growing stream of young people who see the country less as a traditional study destination and more as a structured route into work and long-term opportunity. The 22-year-old graduated from Shinjuku Heiwa Japanese Language School in March and now studies international business at a vocational college in Tokyo. He juggles part-time work as a convenience store clerk and hotel housekeeper to help cover his living expenses. "At first, I was interested in Japanese pop culture," he said. "Then I wanted to learn the language.