A Japan Ground Self-Defense Force Type-88 surface-to-ship missile is fired during the Balikatan exercises at Culili Point Sand Dunes in Paoay, Ilocos Norte province, Philippines, on May 6. | REUTERS Singapore - Defense Minister Shinjiro Koizumi and his Philippine counterpart, Gilberto Teodoro, affirmed Sunday that talks will be launched on the export of surface-to-ship missiles from Japan to the Southeast Asian nation. Koizumi revealed this in talks with reporters after holding a meeting with the Philippine defense chief in Singapore earlier in the day. Type-88 surface-to-ship guided missiles of Japan's Ground Self-Defense Force are expected to be up for consideration. The Philippine side is believed to have shown an interest in procuring the missiles as the Self-Defense Forces used them in the Balikatan multilateral exercises conducted in Manila between April and May. The SDF, which had taken part in the annual exercises organized by the United States and the Philippines as an observer since 2012, joined the drills on a full scale for the first time this year following the entry into force of the Japan-Philippine reciprocal access agreement in September 2025. The possible procurement of Type-88 missiles is expected to help reinforce the deterrent and response capabilities of the Philippines, which is in a territorial dispute with China in the South China Sea.

An accidental leak revealed that Flock, which has cameras in thousands of US communities, is using workers in the Philippines to review and classify footage. Flock, the automatic license plate reader and AI-powered camera company, uses overseas workers from Upwork to train its machine learning algorithms, with training material telling workers how to review and categorize footage including images people and vehicles in the United States, according to material reviewed by 404 Media that was accidentally exposed by the company. The findings bring up questions about who exactly has access to footage collected by Flock surveillance cameras and where people reviewing the footage may be based. Flock has become a pervasive technology in the US, with its cameras present in thousands of communities that cops use every day to investigate things like carjackings. Local police have also performed numerous lookups for ICE in the system.

The archipelago's total bat tally now stands at 85 different flying mammals. Breakthroughs, discoveries, and DIY tips sent every weekday. A few weeks ago, conservationists announced the milestone discovery of the 1500th known bat species, . October's Bat Appreciation Month celebrations apparently aren't done yet. With only a few days remaining before Halloween, a research team has described another new species residing in the Philippines for the journal .

Recent advances in Large Language Models (LLMs) have expanded their role in human interaction, yet curiosity -- a central driver of inquiry -- remains underexplored in these systems, particularly across cultural contexts. In this work, we investigate cultural variation in curiosity using Yahoo! Answers, a real-world multi-country dataset spanning diverse topics. We introduce CUEST (CUriosity Evaluation across SocieTies), an evaluation framework that measures human-model alignment in curiosity through linguistic (style), topic preference (content) analysis and grounding insights in social science constructs. Across open- and closed-source models, we find that LLMs flatten cross-cultural diversity, aligning more closely with how curiosity is expressed in Western countries. We then explore fine-tuning strategies to induce curiosity in LLMs, narrowing the human-model alignment gap by up to 50%. Finally, we demonstrate the practical value of curiosity for LLM adaptability across cultures, showing its importance for future NLP research.

ByteDance's Other AI Chatbot Is Quietly Gaining Traction Around the World ByteDance is paying for ads and partnering with influencers to promote its AI chatbot app Cici in countries like the UK, Mexico, and Indonesia. ByteDance, the parent company of TikTok, has built what is currently the most popular AI chatbot in China: Doubao . Launched in 2023, the app has risen to the top of the country's generative AI market, reaching more than 157 million monthly active users by August, according to Chinese analytics firm QuestMobile. But what's less known is that Doubao also has an overseas counterpart: Cici. It was released around the same time and features a nearly identical female cartoon avatar as its app icon, except Cici's has longer hair than Doubao's.

An increasing number of studies have examined the social bias of rapidly developed large language models (LLMs). Although most of these studies have focused on bias occurring in a single social attribute, research in social science has shown that social bias often occurs in the form of intersectionality -- the constitutive and contextualized perspective on bias aroused by social attributes. In this study, we construct the Japanese benchmark inter-JBBQ, designed to evaluate the intersectional bias in LLMs on the question-answering setting. Using inter-JBBQ to analyze GPT-4o and Swallow, we find that biased output varies according to its contexts even with the equal combination of social attributes.

Manila, Philippines – When former Philippines President Rodrigo Duterte was arrested by the International Criminal Court (ICC) in March, Sheerah Escuerdo spoke to a local television station, welcoming the politician's detention on charges of murder linked to his war on drugs. Escuerdo, who lost her 18-year-old brother, Ephraim, to Duterte's war, clutched a portrait of her sibling during the interview with News 5 Everywhere as she demanded justice for his killing. Days later, she was shocked to find an AI-generated video of her slain brother circulating on Facebook, in which he said he was alive and accused his sister of lying. Are they paying you to do this?" the computer-generated image of Ephraim said. The video, posted online by a pro-Duterte influencer with 11,000 followers, immediately drew thousands of views on Facebook. One of the comments read, "Fake drug war victims". It was Escudero and her brother's image from her News 5 Everywhere interview that the influencer had used to ...

Generating Wikipedia articles autonomously is a challenging task requiring the integration of accurate, comprehensive, and well-structured information from diverse sources. This paper introduces the Memory Organization-based Generation (MOG) framework, a novel approach to address these challenges by leveraging a hierarchical memory architecture. MOG extracts fine-grained memory units from web documents, recursively organizes them into a Wikipedia-style hierarchical structure, and uses this structure to guide the generation process. This ensures alignment between memory and the article outline, improving both informativeness and verifiability while minimizing hallucinations. Additionally, a citation module is implemented to enhance traceability by linking every generated sentence to specific memory units. Evaluations on our newly created WikiStart dataset demonstrate that MOG outperforms baseline methods in producing informative and reliable articles, making it particularly robust in real-world scenarios.

Vision-Language Models (VLMs) are increasingly deployed in diverse cultural contexts, yet their internal biases remain poorly understood. In this work, we propose a novel framework to systematically evaluate how VLMs encode cultural differences and biases related to race, gender, and physical traits across countries. We introduce three retrieval-based tasks: (1) Race to Country retrieval, which examines the association between individuals from specific racial groups (East Asian, White, Middle Eastern, Latino, South Asian, and Black) and different countries; (2) Personal Traits to Country retrieval, where images are paired with trait-based prompts (e.g., Smart, Honest, Criminal, Violent) to investigate potential stereotypical associations; and (3) Physical Characteristics to Country retrieval, focusing on visual attributes like skinny, young, obese, and old to explore how physical appearances are culturally linked to nations. Our findings reveal persistent biases in VLMs, highlighting how visual representations may inadvertently reinforce societal stereotypes.

Multilingual Natural Language Generation (NLG) is challenging due to the lack of training data for low-resource languages. However, some low-resource languages have up to tens of millions of speakers globally, making it important to improve NLG tools for them. Table-to-Text NLG is an excellent measure of models' reasoning abilities but is very challenging in the multilingual setting. System outputs are often not attributable, or faithful, to the data in the source table. Intermediate planning techniques like Question-Answer (QA) blueprints have been shown to improve attributability on summarisation tasks. This work explores whether QA blueprints make multilingual Table-to-Text outputs more attributable to the input tables. This paper extends the challenging multilingual Table-to-Text dataset, TaTA, which includes African languages, with QA blueprints. Sequence-to-sequence language models are then finetuned on this dataset, with and without blueprints. Results show that QA blueprints improve performance for models finetuned and evaluated only on English examples, but do not demonstrate gains in the multilingual setting. This is due to inaccuracies in machine translating the blueprints from English into target languages when generating the training data, and models failing to rely closely on the blueprints they generate. An in-depth analysis is conducted on why this is challenging.