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.

Thailand's army has accused Cambodia of breaching a newly-signed ceasefire deal reached after weeks of deadly clashes that forced nearly one million people from their homes. In a statement, the Thai army said than more than 250 unmanned aerial vehicles (UAVs) were detected flying from the Cambodian side on Sunday night. The ceasefire took effect at noon local time (05:00 GMT) on Saturday. Both sides agreed to freeze the front lines where they are now, ban reinforcements and allow civilians living in border areas to return as soon as possible. It had been seen as a breakthrough, which came after days of talks between both countries, with diplomatic encouragement from China and the US.

The flat-headed felines are the smallest wild cats in Southeast Asia. New images from Thailand's DNP and Panthera prove the existence and rediscovery of one of the world's most Endangered and least known wild cats, the flat-headed cat, in Thailand's Princess Sirindhorn Wildlife Sanctuary. Breakthroughs, discoveries, and DIY tips sent every weekday. Camera traps in Thailand have captured adorable passersby with significant implication for the country's conservation efforts. While these furry creatures might look like your average house cat, they're actually wild flat-headed cats ().

Understanding sentiment in financial documents is crucial for gaining insights into market behavior. These reports often contain obfuscated language designed to present a positive or neutral outlook, even when underlying conditions may be less favorable. This paper presents a novel approach using Aspect-Based Sentiment Analysis (ABSA) to decode obfuscated sentiment in Thai financial annual reports. We develop specific guidelines for annotating obfuscated sentiment in these texts and annotate more than one hundred financial reports. We then benchmark various text classification models on this annotated dataset, demonstrating strong performance in sentiment classification. Additionally, we conduct an event study to evaluate the real-world implications of our sentiment analysis on stock prices. Our results suggest that market reactions are selectively influenced by specific aspects within the reports. Our findings underscore the complexity of sentiment analysis in financial texts and highlight the importance of addressing obfuscated language to accurately assess market sentiment.

Accurate rainfall forecasting, particularly for extreme events, remains a significant challenge in climatology and the Earth system. This paper presents novel physics-informed Graph Neural Networks (GNNs) combined with extreme-value analysis techniques to improve gauge-station rainfall predictions across Thailand. The model leverages a graph-structured representation of gauge stations to capture complex spatiotemporal patterns, and it offers explainability through teleconnections. We preprocess relevant climate indices that potentially influence regional rainfall. The proposed Graph Attention Network with Long Short-Term Memory (Attention-LSTM) applies the attention mechanism using initial edge features derived from simple orographic-precipitation physics formulation. The embeddings are subsequently processed by LSTM layers. To address extremes, we perform Peak-Over-Threshold (POT) mapping using the novel Spatial Season-aware Generalized Pareto Distribution (GPD) method, which overcomes limitations of traditional machine-learning models. Experiments demonstrate that our method outperforms well-established baselines across most regions, including areas prone to extremes, and remains strongly competitive with the state of the art. Compared with the operational forecasting system SEAS5, our real-world application improves extreme-event prediction and offers a practical enhancement to produce high-resolution maps that support decision-making in long-term water management.

Although Large Language Models have demonstrated powerful capabilities in a wide range of tasks such as language understanding and code generation, the frequent occurrence of hallucinations during the generation process has become a significant impediment to their deployment in critical application scenarios. Current mainstream hallucination detection methods rely on factual consistency verification or static hidden layer features. The former is constrained by the scope of knowledge coverage, while the latter struggles to capture reasoning biases during the inference process. To address these issues, and inspired by signal analysis methods in cognitive neuroscience, this paper proposes a hallucination detection method based on the frequency-domain analysis of hidden layer temporal signals, named HSAD (\textbf{H}idden \textbf{S}ignal \textbf{A}nalysis-based \textbf{D}etection). First, by treating the LLM's reasoning process as a cognitive journey that unfolds over time, we propose modeling and simulating the human process of signal perception and discrimination in a deception-detection scenario through hidden layer temporal signals. Next, The Fast Fourier Transform is applied to map these temporal signals into the frequency domain to construct spectral features, which are used to capture anomalies that arise during the reasoning process; analysis experiments on these spectral features have proven the effectiveness of this approach. Finally, a hallucination detection algorithm is designed based on these spectral features to identify hallucinations in the generated content. By effectively combining the modeling of the reasoning process with frequency-domain feature extraction, the HSAD method overcomes the limitations of existing approaches in terms of knowledge coverage and the detection of reasoning biases, demonstrating higher detection accuracy and robustness.

The alignment of large language models (LLMs) aims to ensure their outputs adhere to human values, ethical standards, and legal norms. Traditional alignment methods often rely on resource-intensive fine-tuning (FT), which may suffer from knowledge degradation and face challenges in scenarios where the model accessibility or computational resources are constrained. In contrast, training-free (TF) alignment techniques--leveraging in-context learning, decoding-time adjustments, and post-generation corrections--offer a promising alternative by enabling alignment without heavily retraining LLMs, making them adaptable to both open-source and closed-source environments. This paper presents the first systematic review of TF alignment methods, categorizing them by stages of pre-decoding, in-decoding, and post-decoding. For each stage, we provide a detailed examination from the viewpoint of LLMs and multimodal LLMs (MLLMs), highlighting their mechanisms and limitations. Furthermore, we identify key challenges and future directions, paving the way for more inclusive and effective TF alignment techniques. By synthesizing and organizing the rapidly growing body of research, this survey offers a guidance for practitioners and advances the development of safer and more reliable LLMs.

Thailand's top court has ruled that former prime minister Thaksin Shinawatra must serve a year in jail, in yet another blow to the influential political dynasty. The decision relates to a previous case where he was sentenced to years in prison for corruption, but ended up spending less than a day in a jail cell as he was moved to a hospital. On Tuesday, the Supreme Court ruled that this transfer was unlawful - and that the 76-year-old would have to serve his sentence in jail. Thaksin and his family have dominated Thai politics since he was first elected PM in 2001. His daughter Paetongtarn previously served as leader but was removed from office last month over a leaked phone call.

Large language models (LLMs) are increasingly used to simulate human decision-making, but their intrinsic biases often diverge from real human behavior--limiting their ability to reflect population-level diversity. We address this challenge with a persona-based approach that leverages individual-level behavioral data from behavioral economics to adjust model biases. Applying this method to the ultimatum game--a standard but difficult benchmark for LLMs--we observe improved alignment between simulated and empirical behavior, particularly on the responder side. While further refinement of trait representations is needed, our results demonstrate the promise of persona-conditioned LLMs for simulating human-like decision patterns at scale.

English is the predominant language on the web, powering nearly half of the world's top ten million websites. Support for multilingual content is nevertheless growing, with many websites increasingly combining English with regional or native languages in both visible content and hidden metadata. This multilingualism introduces significant barriers for users with visual impairments, as assistive technologies like screen readers frequently lack robust support for non-Latin scripts and misrender or mispronounce non-English text, compounding accessibility challenges across diverse linguistic contexts. Yet, large-scale studies of this issue have been limited by the lack of comprehensive datasets on multilingual web content. To address this gap, we introduce LangCrUX, the first large-scale dataset of 120,000 popular websites across 12 languages that primarily use non-Latin scripts. Leveraging this dataset, we conduct a systematic analysis of multilingual web accessibility and uncover widespread neglect of accessibility hints. We find that these hints often fail to reflect the language diversity of visible content, reducing the effectiveness of screen readers and limiting web accessibility. We finally propose Kizuki, a language-aware automated accessibility testing extension to account for the limited utility of language-inconsistent accessibility hints.