Kimi Onoda, minister for intellectual property strategy, speaks to reporters Tuesday about the government's adoption of guiding principles on intellectual rights protection. The government on Tuesday adopted guiding principles on intellectual property protection for generative artificial intelligence operators, calling on such businesses to disclose an outline of data and methods used to train their AI tools. The Principles Code, while not legally binding, is designed to boost transparency related to AI and promote both the protection of intellectual property rights and innovation amid the rapidly spreading use of the cutting-edge technology. Businesses that accept all or part of the code will notify the government and disclose the learning processes, types of learning data and methods of collecting such data for their generative AI models on their websites. The code covers not only domestic businesses but also overseas operators that provide AI systems and services in Japan. There are growing concerns that texts, images and other materials are being used by generative AI for learning without permission, potentially resulting in intellectual property rights violations.

Meta's little vibe-coding app Pocket has now arrived in the US after a brief testing phase in Brazil. The app lets people generate minigames, called gizmos, via prompts. The games get published to a scrollable feed, bringing in a social media component. Users can save gizmos to a favorites list and repost gizmos to their feed. These games make full use of the capabilities available in modern smartphones.

Japan is considering setting out a nonbinding code for generative artificial intelligence businesses to encourage them to disclose their AI training data and methods for collecting such data to the public. A government panel Tuesday broadly approved a plan to adopt what is known as a "principle code" for generative artificial intelligence businesses to protect intellectual property rights by urging firms to disclose their AI training data and methods for collecting such data to the public. A draft code was presented at an online meeting of an expert panel on intellectual property rights in the AI era. It is based on a law on AI-related technology enacted in May 2025 and seeks to balance rights protection with technological innovation. The government will use a "comply or explain" approach, under which it will set out a nonbinding code for generative AI businesses, including system developers and service providers, allowing them to choose either to comply with the code or publicly explain why they will not comply. Firms that decide to comply with the code will announce their compliance on their websites and notify the government.

By 2050, scientists estimate that antibiotic-resistant infections will be associated with more than eight million deaths around the world every year. These are bacterial infections that resist traditional antibiotics like penicillin. They can develop when you eat contaminated food, have an open wound or undergo surgery. E. coli is a good example, as several strains have become highly resistant to conventional antibiotics . They can also arrive as secondary infections, like pneumonia after a virus .

Misato Kaetsu shows on a smartphone an online information board that she created following a major earthquake in Kumamoto Prefecture last month. KUMAMOTO - Generative artificial intelligence (AI) has been increasingly utilized to assist relief efforts in the wake of a powerful earthquake in Kumamoto Prefecture late last month. At the same time, AI is believed to have been used to create fake videos and other misinformation that were spread on social media, highlighting both the advantages and drawbacks of the technology. Misato Kaetsu, 38, who experienced the quake in the Kumamoto town of Mifune, created an online information board using generative AI on her smartphone while at the evacuation center where she and her family had fled. Those affected by the quake, which registered up to 7, the highest level on Japan's seismic intensity scale, can use the site to share local information, such as the locations of reopened stores and emergency feeding stations. The service was launched just hours after the idea was conceived.

Damage caused by the use of generative AI is becoming a serious concern, including from AI covers, in which people have AI tools learn the voices of singers and voice actors, and sing songs using the professionals' voices. The Justice Ministry has come up with a draft report calling for the protection of the voices and images of famous individuals as the use of generative artificial intelligence grows. The draft report on civil responsibility over the unauthorized use of portraits and voices of famous people was submitted to an expert committee Monday. The ministry will release a final report as early as August after receiving expert feedback. Many things remain unclear regarding what constitutes the illegal use of voices, as no Japanese court ruling has been issued on related rights. The ministry said that it hopes the final report will be used as a reference in lawsuits and AI development.

More people are using generative artificial intelligence to make travel plans for their summer vacation and letting their children use the technology when doing their homework during summer holidays. Six out of 10 people who responded to a survey on this year's summer holidays said they are using generative artificial intelligence to make travel plans. The survey, conducted by Meiji Yasuda Life Insurance on 1,120 people in their 20s to 50s in June, showed that 61.2% of those planning to travel in Japan or abroad refer to generative AI to make travel itineraries, as well as obtain information on local food and transportation. "The main tool people use for planning trips and doing research when they get there is shifting from travel guidebooks to generative AI," the firm said. Asked how they plan to spend their summer holidays, 58.4% said they are going out, down by 6.3 percentage points from last year. The rate of those traveling in Japan was 57.6%, up by 1 percentage point, while the ratio of those traveling overseas halved from 13.5% last year to 6.4%.

Meta appears to have soft-launched a new app called Pocket that's aimed at getting people to vibe-code their own minigames. Mobile developer and reverse engineer Alessandro Paluzzi spotted Pocket and posted about it to X today, but reporting platform AppFigures told TechCrunch that the app has been available on both iOS and Android since June 29. Though the app is listed publicly, it's not available in the US on any of the half dozen phone models associated with our Google accounts, and a help page on Meta's site says the Pocket app is not yet available everywhere. The company has not made any public announcement yet about the launch or where the app is being trialed. We've reached out for comment and will update this post if we receive a response.

Kornelius Raeth 1 Nicole Ludwig 1 2 Abstractscoring rules distribute the training gradient in proportion to Sample-based generative models are increasingly data density, with no awareness of the decision maker's cost structure. The model's limited capacity is allocated globused for probabilistic forecasting in high-stakes ally, leaving decision-critical regions of the output space decision settings, yet their training objectives are potentially underserved. These models are commonly trained with strictly proper Given a forecast, a decision maker with cost function c(a,y), scoring rules, such as the energy score, which al-of action aand outcome y, selects the action that minimises locate their training signal in proportion to dataexpected cost under the forecast distribution; a point forecast density, with no awareness of where forecast eris insufficient to evaluate this expectation. A good forecast rors are most costly for downstream decisions. Crucially, the energy score objective with a differentiable deci-observed cost of the optimal action is itself a proper scoring sion loss that directly penalises the cost incurredrule (Hartline et al., 2025; Kleinberg et al., 2023), placing by acting on the model's forecast. This combinedit in the same family as the energy score which licenses loss is theoretically grounded, as the decision losstheir combination as a theoretically well-founded training is itself a proper scoring rule. Introduction score acts as that anchor, preventing the model from collapsing outside cost-sensitive regions. Our method is theo-tion based on a temperature forecast, balancing asset loss against the cost of intervention. In the weather domain, retically grounded and leads to better downstream decisions state-of-the-art forecasting systems (Lang et al., 2024; Pricewhile retaining full probabilistic forecasts, as validated on et al., 2023) are trained with strictly proper scoring rulessynthetic and real-world forecasting tasks. A gradient analysis showing which regions benefitscore reduces to the continuous ranked probability score from the decision loss and why, based on the cost (CRPS), widely used in meteorological forecast verificafunction structure. Both model classes introduced above are commonly trained by minimising strictly proper sion calibration.

Inference-time scaling is a promising paradigm to improve generative models, especially when outputs must satisfy structural constraints or optimize downstream rewards. We consider Masked Diffusion Model (MDM) and introduce MDM-VGB, a discrete diffusion sampler that augments unmasking generation with theoretically principled reward-guided remasking. Inspired by the recent success of the classical Jerrum-Sinclair backtracking Markov chain in reward-tilted generation, MDM-VGB extends the backtracking random walk from a fixed prefix tree to a masked-state graph, allowing tokens to be unmasked and remasked at arbitrary positions. The resulting sampler favors unmasking and remasking moves that lead to higher-value partial configurations, enabling both effective high-reward generation and efficient repair of low-reward samples. We prove that MDM-VGB is robust to process-verifier noise and achieves quadratic complexity, while popular test-time heuristics such as best-of-$N$ can incur exponential complexity due to error accumulation. Our theoretical findings are corroborated by strong empirical performance, particularly on popular constraint-satisfaction and scientific benchmarks such as Sudoku and QM9.