Such AI-generated content attracted approximately 335 million views on social media, resulting in financial losses estimated at ¥2 billion to ¥4.5 billion for celebrities and artists, according to the study. The estimated losses were calculated based on licensing fees related to using a person's likeness or voice, as well as the advertising value of view counts. However, the nonprofit added that the "actual financial losses might be significantly larger than the estimate," as the calculation only covered cases they were able to find. Only 1.1% of companies said they had guidelines on how to deal with these violations. Some 52% said they were "currently considering" options, while the rest had no plans as of date.

Chief Cabinet Secretary Minoru Kihara (third from left) speaks at a meeting of the digital administrative and fiscal reform council, held at the Prime Minister's Office in Tokyo on Tuesday. The government decided Tuesday to establish a new council to drastically overhaul legal frameworks governing the development and use of artificial intelligence. The plan was included in the government's 2026 basic policy guidelines adopted at a meeting of the digital administrative and fiscal reform council, held at the Prime Minister's Office in Tokyo. This council, launched under the administration of former Prime Minister Fumio Kishida, will be reorganized into the new body. The guidelines stressed the urgency of advancing what is described as AI transformation, or a fundamental review of work using AI, to cope with population decline.

Touted as a less-hookup-focused Grindr, Goose is an invite-only space for gay men. The problem is the people promoting it don't seem real. "You're receiving this because you're exactly the type of person we're building this for," the caption reads, accompanied by a code for an invite to a "members only community." The link leads to a login for Goose, a dating and friendship app for gay men with the slogan "for the boys," which allows users to "meet guys through the life you already have," according to its website. Neither does @danielmmulugeta, the cute dark-haired influencer who shared the above caption, with the exact same verbiage, on Close Friends' Stories.

The Sliced Wasserstein (SW) distance has emerged as a computationally attractive alternative to the Wasserstein distance by leveraging one-dimensional optimal transport along random projections. Standard estimators of the SW distance rely on Monte Carlo averages of one-dimensional Wasserstein distances computed via quantile functions, which require sorting projected samples and access to full datasets. In this work, we introduce a new class of estimators for the Sliced Wasserstein distance based on cumulative distribution functions (CDFs) of projected measures, that avoid sorting and scale via massive dataset parallelism. This class includes several estimators, some of them being indexed by hyperparameters controlling their variance or smoothness. We show that they are especially well suited to scenarios in which CDFs are more tractable than quantile functions, such as mixtures of Gaussians, and moreover that they are also naturally compatible with federated learning, since CDFs of projected data can be computed and aggregated locally without requiring the exchange of raw samples.

The essence of 3DGaussian Splatting (3DGS) training is to smartly allocate Gaussian primitives, expressing complex regions with more primitives and vice versa. Prior researches typically mark out under-reconstructed regions in a renderingloss-driven manner. However, such a loss-driven strategy is often dominated by low-frequency regions, which leads to insufficient modeling of high-frequency details in texture-rich regions. As a result, it yields a suboptimal spatial allocation of Gaussian primitives. This inspires us to excavate the loss-agnostic visual prior in training views to identify complex regions that need more primitives to model.

Diffusion models have demonstrated remarkable capabilities in generating highquality images. Recent advancements in Layout-to-Image (L2I) generation have leveraged positional conditions and textual descriptions to facilitate precise and controllable image synthesis.

Large vision-language models (LVLMs) derive their capabilities from extensive training on vast corpora of visual and textual data.

We address the problem of causal effect estimation in the presence of hidden confounders, using nonparametric instrumental variable (IV) regression. A leading strategy employs spectral features - that is, learned features spanning the top eigensubspaces of the operator linking treatments to instruments. We derive a generalization error bound for a two-stage least squares estimator based on spectral features, and gain insights into the method's performance and failure modes. We show that performance depends on two key factors, leading to a clear taxonomy of outcomes. In a good scenario, the approach is optimal. This occurs with strong spectral alignment, meaning the structural function is well-represented by the top eigenfunctions of the conditional operator, coupled with this operator's slow eigenvalue decay, indicating a strong instrument. Performance degrades in a bad scenario: spectral alignment remains strong, but rapid eigenvalue decay (indicating a weaker instrument) demands significantly more samples for effective feature learning. Finally, in the ugly scenario, weak spectral alignment causes the method to fail, regardless of the eigenvalues' characteristics.

Recent large-scale reasoning models have achieved state-of-the-art performance on challenging mathematical benchmarks, yet the internal mechanisms underlying their success remain poorly understood. In this work, we introduce the notion of a reasoning graph, extracted by clustering hidden-state representations at each reasoning step, and systematically analyze three key graph-theoretic properties: cyclicity, diameter, and small-world index, across multiple tasks (GSM8K, MATH500, AIME 2024). Our findings reveal that distilled reasoning models (e.g., DeepSeekR1-Distill-Qwen-32B) exhibit significantly more recurrent cycles (about 5 per sample), substantially larger graph diameters, and pronounced small-world characteristics (about 6x) compared to their base counterparts. Notably, these structural advantages grow with task difficulty and model capacity, with cycle detection peaking at the 14B scale and exploration diameter maximized in the 32B variant, correlating positively with accuracy. Furthermore, we show that supervised fine-tuning on an improved dataset systematically expands reasoning graph diameters in tandem with performance gains, offering concrete guidelines for dataset design aimed at boosting reasoning capabilities.

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