Thailand, Cambodia agree to build on ceasefire in talks in China's Yunnan Thailand and Cambodia plan to rebuild mutual trust and consolidate a ceasefire, Beijing says at the end of two days of talks in southwestern China, despite new accusations from the Thai military that its Cambodian counterparts are violating the truce with drone flights. The foreign ministers of Thailand and Cambodia met with the Chinese foreign minister in Yunnan province on Monday for the scheduled two days of talks aimed at ending weeks of fierce fighting along their border that has killed more than 100 people and displaced more than half a million civilians in both countries. As part of the deal, Thailand has agreed to return 18 captured Cambodian soldiers on Tuesday if the ceasefire, which took effect at noon (05:00 GMT) on Saturday, is fully observed. Speaking to reporters after the meeting, Thai Foreign Minister Sihasak Phuangketkeow said he believed the parties were "moving in a positive direction". "We haven't resolved everything, but I think we are making progress in the right direction, and we have to keep up the momentum," he said.

Things to Do in L.A. Tap to enable a layout that focuses on the article. 'Memory manipulation is inevitable': How rewriting memory in the lab might one day heal humans Professor and neuroscientist Steve Ramirez, shown working with brain samples, is exploring the science of memory manipulation. This is read by an automated voice. Please report any issues or inconsistencies here . Scientists have found that memories are not static records but dynamic processes that change the brain's wiring each time they are recalled.

Capable of creating "nearly perfect" face swaps during live video chats, Hoatian has made millions, mainly via Telegram. But its main channel vanished after WIRED's inquiry into scammers using the app. The Chinese-language artificial intelligence app Haotian is so effective that it's made millions of dollars selling its face-swapping technology on Telegram . The service integrates easily with messaging platforms like WhatsApp and WeChat and claims that users can tweak up to 50 settings--including the ability to adjust things like cheekbone size and eye position--to help mimic the face they are impersonating. But while Haotian is a robust and versatile platform, researchers and WIRED's own analysis have found that the service has been marketing to so-called "pig butchering" scammers and those running online fraud operations in Southeast Asia.

Compared to English and other high-resource languages, spellchecking for Khmer remains an unresolved problem due to several challenges. First, there are misalignments between words in the lexicon and the word segmentation model. Second, a Khmer word can be written in different forms. Third, Khmer compound words are often loosely and easily formed, and these compound words are not always found in the lexicon. Fourth, some proper nouns may be flagged as misspellings due to the absence of a Khmer named-entity recognition (NER) model. Unfortunately, existing solutions do not adequately address these challenges. This paper proposes a holistic approach to the Khmer spellchecking problem by integrating Khmer subword segmentation, Khmer NER, Khmer grapheme-to-phoneme (G2P) conversion, and a Khmer language model to tackle these challenges, identify potential correction candidates, and rank the most suitable candidate. Experimental results show that the proposed approach achieves a state-of-the-art Khmer spellchecking accuracy of up to 94.4%, compared to existing solutions. The benchmark datasets for Khmer spellchecking and NER tasks in this study will be made publicly available.

Khmer polarity classification is a fundamental natural language processing task that assigns a positive, negative, or neutral label to a given Khmer text input. Existing Khmer models typically predict the label without explaining the rationale behind the prediction. This paper proposes an explainable Khmer polarity classifier by fine-tuning an instruction-based reasoning Qwen-3 model. The notion of explainability in this paper is limited to self-explanations, which the model uses to rationalize its predictions. Experimental results show that the fine-tuned model not only predicts labels accurately but also provides reasoning by identifying polarity-related keywords or phrases to support its predictions. In addition, we contribute a new Khmer polarity dataset consisting of short- to medium-length casual, romanized, and mixed-code Khmer expressions. This dataset was constructed using both heuristic rules and human curation and is publicly available through a gated Hugging Face repository (rinabuoy/khmerpolarity_nonreasoning). The fine-tuned Qwen-3 models are also made available in the same Hugging Face account.

Evaluating text generation capabilities of large language models (LLMs) is challenging, particularly for low-resource languages where methods for direct assessment are scarce. We propose MUG-Eval, a novel framework that evaluates LLMs' multilingual generation capabilities by transforming existing benchmarks into conversational tasks and measuring the LLMs' accuracies on those tasks. We specifically designed these conversational tasks to require effective communication in the target language. Then, we simply use task success rate as a proxy for successful conversation generation. Our approach offers two key advantages: it is independent of language-specific NLP tools or annotated datasets, which are limited for most languages, and it does not rely on LLMs-as-judges, whose evaluation quality degrades outside a few high-resource languages. We evaluate 8 LLMs across 30 languages spanning high, mid, and low-resource categories, and we find that MUG-Eval correlates strongly with established benchmarks ($r$ > 0.75) while enabling standardized comparisons across languages and models. Our framework provides a robust and resource-efficient solution for evaluating multilingual generation that can be extended to thousands of languages.

Tonogenesis-the historical process by which segmental contrasts evolve into lexical tone-has traditionally been studied through comparative reconstruction and acoustic phonetics. We introduce a computational approach that quantifies the functional role of pitch at different stages of this sound change by measuring how pitch manipulation affects automatic speech recognition (ASR) performance. Through analysis on the sensitivity to pitch-flattening from a set of closely related Tibetan languages, we find evidence of a tonogenesis continuum: atonal Amdo dialects tolerate pitch removal the most, while fully tonal U-Tsang varieties show severe degradation, and intermediate Kham dialects fall measurably between these extremes. These gradient effects demonstrate how ASR models implicitly learn the shifting functional load of pitch as languages transition from consonant-based to tone-based lexical contrasts. Our findings show that computational methods can capture fine-grained stages of sound change and suggest that traditional functional load metrics, based solely on minimal pairs, may overestimate pitch dependence in transitional systems where segmental and suprasegmental cues remain phonetically intertwined.

Despite growing research on bias in large language models (LLMs), most work has focused on gender and race, with little attention to religious identity. This paper explores how religion is internally represented in LLMs and how it intersects with concepts of violence and geography. Using mechanistic interpretability and Sparse Autoencoders (SAEs) via the Neuronpedia API, we analyze latent feature activations across five models. We measure overlap between religion- and violence-related prompts and probe semantic patterns in activation contexts. While all five religions show comparable internal cohesion, Islam is more frequently linked to features associated with violent language. In contrast, geographic associations largely reflect real-world religious demographics, revealing how models embed both factual distributions and cultural stereotypes. These findings highlight the value of structural analysis in auditing not just outputs but also internal representations that shape model behavior.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Health Secretary Robert F. Kennedy Jr. speaks about autism in the White House on Monday as President Trump and Centers for Medicare & Medicaid Services Administrator Dr. Mehmet Oz look on. This is read by an automated voice. Please report any issues or inconsistencies here . On Monday, President Trump led a White House press event where he and many of his administration's health leaders told the public that taking Tylenol during pregnancy increases the risk of autism in children.

Things to Do in L.A. Tap to enable a layout that focuses on the article. A woman in the audience wears a red hat that reads Make America Healthy Again during a Senate Homeland Security and Government Affairs Subcommittee Hearing on Capitol Hill on September 9, 2025 in Washington, DC. The hearing was titled "how the corruption of science has impacted public perception and policies regarding vaccines." Voice comes from the use of AI. Please report any issues or inconsistencies here .