We consider a contextual bandit problem with S contexts and K actions. In each round t = 1,2,... the learner observes a random context and chooses an action based on its past experience. The learner then observes a random reward whose mean is a function of the context and the action for the round. Under the assumption that the contexts can be lumped into r min{S,K}groups such that the mean reward for the various actions is the same for any two contexts that are in the same group, we give an algorithm that outputs an ε-optimal policy after using at most eO(r(S+K)/ε2) samples with high probability and provide a matching Ω(r(S + K)/ε2) lower bound. In the regret minimization setting, we give an algorithm whose cumulative regret up to time T is bounded by eO( p r3(S+K)T). To the best of our knowledge, we are the first to show the near-optimal sample complexity in the PAC setting and eO( p poly(r)(S+K)T)minimax regret in the online setting for this problem. We also show our algorithms can be applied to more general low-rank bandits and get improved regret bounds in some scenarios.

The extraordinary ability of generative models to generate photographic images has intensified concerns about the spread of disinformation, thereby leading to the demand for detectors capable of distinguishing between AI-generated fake images and real images. However, the lack of large datasets containing images from the most advanced image generators poses an obstacle to the development of such detectors. In this paper, we introduce the GenImage dataset, which has the following advantages: 1) Plenty of Images, including over one million pairs of AI-generated fake images and collected real images.

Some of the most successful knowledge graph embedding (KGE) models for link prediction - CP, RESCAL, TUCKER, COMPLEX - can be interpreted as energy-based models. Under this perspective they are not amenable for exact maximum-likelihood estimation (MLE), sampling and struggle to integrate logical constraints. This work re-interprets the score functions of these KGEs as circuits - constrained computational graphs allowing efficient marginalisation. Then, we design two recipes to obtain efficient generative circuit models by either restricting their activations to be non-negative or squaring their outputs. Our interpretation comes with little or no loss of performance for link prediction, while the circuits framework unlocks exact learning by MLE, efficient sampling of new triples, and guarantee that logical constraints are satisfied by design.

AI models surpassed the highest score recorded for a human test taker in this year's University of Tokyo entrance exam, a new study shows. If an artificial intelligence model such as ChatGPT had taken the entrance exams for Japan's top university in 2026, it would have been assessed as top of the class and admitted for scoring higher than any human test takers, a study by AI startup LifePrompt has found. The research used three major AI models -- ChatGPT 5.2 Thinking by OpenAI, Gemini 3 Pro Preview by Google and Claude Opus 4.5 by Anthropic -- and had them take the actual entrance exam used by the University of Tokyo in February 2026 to assess candidates for courses set to start in April. The university's category 3 science exam, often taken by those who want to enter the institution's medical school, is considered the most difficult exam to pass in Japan. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Beijing's insistence that Meta unwind its deal with a Chinese A.I. start-up marks an escalation in the geopolitical fight over advanced tech. TAIPEI - Manus, an artificial intelligence startup, began with an idea among three engineers in Wuhan, China, united by an obsession with AI and a shared ambition to build a global venture. From the outset, they looked beyond China. Their big break came in March last year. Manus had drawn the attention of Silicon Valley investors with an AI agent capable of carrying out tasks on its own.