The limited predictive skill of forecasts makes it difficult for decision-makers to act decisively. Advance assessment of real-time forecast credibility can strengthen decision-makers' resolve and confidence to act. Such an assessment can draw on real-time observations of large-scale background signals. This study evaluates how credible the 2026 East Asia summer temperature forecast is. Enhanced predictability of East Asia summer temperature can be indicated by the synergistic forcing of sea surface temperature anomalies (SSTAs) across three key oceanic regions: the tropical western Pacific, the Japan Sea-Kuroshio-Kuroshio Extension (K-KE), and the North Atlantic. Based on the latest observational data and model predictions, the SSTAs in these three regions maintain positive anomalies, which suggests that East Asia's summer temperature forecast skill will stay at a relatively high level in the coming summer. Based on the predictions, the following summer is expected to feature pronounced positive temperature anomalies over central and eastern China, the Korean Peninsula, and Japan, which may trigger regional droughts and place severe strain on power supply networks.
Rapid developments of large language models have revolutionized many NLP tasks for English data. Unfortunately, the models and their evaluations for low-resource languages are being overlooked, especially for languages in South Asia. Although there are more than 650 languages in South Asia, many of them either have very limited computational resources or are missing from existing language models. Thus, a concrete question to be answered is: Can we assess the current stage and challenges to inform our NLP community and facilitate model developments for South Asian languages? In this survey, we have comprehensively examined current efforts and challenges of NLP models for South Asian languages by retrieving studies since 2020, with a focus on transformer-based models, such as BERT, T5, & GPT. We present advances and gaps across 3 essential aspects: data, models, & tasks, such as available data sources, fine-tuning strategies, & domain applications. Our findings highlight substantial issues, including missing data in critical domains (e.g., health), code-mixing, and lack of standardized evaluation benchmarks. Our survey aims to raise awareness within the NLP community for
Purpose: This study compares the hierarchical structure of scientific teams across countries and investigates factors associated with the observed cross-national differences. Design/methodology/approach: Drawing on 150,817 publications with author contribution statements, we focus on the 15 countries with the largest volume of scientific publications, examine cross-country variations in the proportion of tall teams, and analyze how this proportion correlates with other factors. Findings: Scientific output from East Asia is dominated by tall teams, which persist after controlling for team size, indicating that this pattern cannot be fully accounted for by the prevalence of larger teams in these countries. Cultural factors, measured by Power Distance, as well as the observed funding patterns of major basic science agencies, are associated with the dominance of tall teams in East Asia. Research limitations: This study is limited by its reliance on publications with author contribution statements, which may introduce selection bias; its focus on cultural and funding factors, while leaving other institutional contexts unexamined; and its use of a leadership concentration measure that do
Over the years, research in system identification has provided a rich set of methods for learning dynamical models, together with well-established theoretical guarantees. In practice, however, the choice of model class, training algorithm, and hyperparameter tuning is still largely left to empirical trial-and-error, requiring substantial expert time and domain experience. Motivated by recent advances in agentic artificial intelligence, we present ASIA, a framework that delegates this iterative search to a large language model acting as an autonomous coding agent. Building on existing agentic platforms, ASIA closes the loop between hypothesis, implementation, and evaluation without human intervention, requiring only a plain-English description of the identification problem. We conduct an empirical study of ASIA on two system identification benchmarks and analyse the agent's search behaviour, the architectures and training strategies it discovers, and the quality of the resulting models. We also discuss the potential of the approach and its current limitations, including implicit test leakage, reduced methodological transparency, and reproducibility concerns.
This study analyzes and predicts air pollution in Asia, focusing on PM 2.5 levels from 2018 to 2023 across five regions: Central, East, South, Southeast, and West Asia. South Asia emerged as the most polluted region, with Bangladesh, India, and Pakistan consistently having the highest PM 2.5 levels and death rates, especially in Nepal, Pakistan, and India. East Asia showed the lowest pollution levels. K-means clustering categorized countries into high, moderate, and low pollution groups. The ARIMA model effectively predicted 2023 PM 2.5 levels (MAE: 3.99, MSE: 33.80, RMSE: 5.81, R: 0.86). The findings emphasize the need for targeted interventions to address severe pollution and health risks in South Asia.
Amid the International Year of Quantum Science and Technology 2025 (IYQ 2025), a significant portion of global funding has been dedicated to various quantum initiatives, with over 30 countries announcing their respective quantum strategies. Within the Southeast Asia context, Singapore, Thailand, and the Philippines have launched their respective quantum strategies and roadmaps. Meanwhile, six out of eleven Southeast Asia countries have expressed interest in formulating a regional quantum ecosystem to pursue a set of common goals. Quantum technologies, though still in their infancy within the second quantum revolution, have advanced rapidly in recent years. Due to their dual-use nature, quantum technologies are considered emerging and disruptive, often raising concerns from the cybersecurity perspective. While several discussions regarding Malaysia's quantum initiative and strategy are ongoing, it is vital to broaden the conversation and position Malaysia within the regional ecosystem. This paper provides an overview of Malaysia's quantum landscape and a summary of the regional initiatives since the establishment of Southeast Asia Quantum Network. We then analyse Malaysia's strength
We introduce SeaLLMs-Audio, the first large audio-language model (LALM) tailored for multiple Southeast Asian (SEA) languages-Indonesian (id), Thai (th), and Vietnamese (vi)-alongside English (en) and Chinese (zh). Trained on a large-scale audio corpus, SeaLLMs-Audio exhibits strong performance across diverse audio-centric tasks, spanning fine-grained audio understanding and voice-based interaction. Its key features include: 1) Multilingual: the model primarily supports 5 languages, namely Indonesian, Thai, Vietnamese, English, and Chinese; 2) Multimodal: the model accepts flexible input modalities, including audio only, text only, as well as audio with text; 3) Multi-task: the model supports a wide range of tasks, including audio analysis tasks such as Audio Captioning, Automatic Speech Recognition, Speech-to-Text Translation, Speech Emotion Recognition, Speech Question Answering, and Speech Summarization. It also enables voice-based dialogue, including answering factual, mathematical, and general knowledge queries. As a significant step towards advancing audio LLMs in Southeast Asia, we expect SeaLLMs-Audio to benefit both the regional research community and industry. To automate
The East Asia VLBI Network (EAVN) has recently enabled dual-polarization observations at $22$ and $43\,\mathrm{GHz}$. We present the first systematic verification of its polarimetric performance using EAVN observations of M87, 3C 279, 3C 273, and OJ 287, calibrated with the GPCAL pipeline and evaluated against near-contemporaneous VLBA images at comparable frequencies. Most stations show stable polarimetric leakages with amplitudes of $5$-$10\%$ over monthly timescales. While several VERA stations exhibit D-term phase variations between epochs, we attribute these to field-rotator (FR) offsets and demonstrate that phase stability is restored after applying the analytically derived FR corrections. The resulting linear-polarization morphologies and EVPAs broadly agree with the VLBA results within uncertainties; fractional polarization measured by the EAVN tends to be slightly higher near polarization peaks. Although exact one-to-one comparisons are limited by moderate frequency and epoch differences, the combined evidence indicates robust EAVN polarimetric calibration and imaging capabilities at $22$ and $43\,\mathrm{GHz}$. These results support the scientific capability of EAVN polar
This study investigates the impact of artificial intelligence (AI) technology on cross-border trade using a qualitative content analysis approach. By synthesizing existing empirical studies, we aim to quantify the overall effect of AI on trade flows and identify the key moderating and mediating variables. Besides, our results show that AI adoption significantly increases trade volumes in Southeast Asia. Likewise, these effects are stronger in regions with advanced technological infrastructure and favorable regulatory frameworks. In addition, Trade firm size partially mediates the relationship between AI technology and trade performance. Furthermore, this study draws on several key theoretical frameworks that provide a comprehensive understanding of the mechanisms through which AI technology is affecting cross-border trade in Southeast Asia. The primary theories used in this research include the technology, organization, and environment (TOE) framework, the diffuse innovation (DOI) theory, Dynamic Capabilities Theory, Comparative Advantage Theory, Network theory, Transaction Cost Economics (TCE), the resource-based view, and the institution theory. Consequently, this study contribut
Artificial intelligence (AI) trends vary significantly across global regions, shaping the trajectory of innovation, regulation, and societal impact. This variation influences how different regions approach AI development, balancing technological progress with ethical and regulatory considerations. This study conducts a comparative analysis of AI trends in the United States (US), the European Union (EU), and Asia, focusing on three key dimensions: generative AI, ethical oversight, and industrial applications. The US prioritizes market-driven innovation with minimal regulatory constraints, the EU enforces a precautionary risk-based framework emphasizing ethical safeguards, and Asia employs state-guided AI strategies that balance rapid deployment with regulatory oversight. Although these approaches reflect different economic models and policy priorities, their divergence poses challenges to international collaboration, regulatory harmonization, and the development of global AI standards. To address these challenges, this paper synthesizes regional strengths to propose an adaptive AI governance framework that integrates risk-tiered oversight, innovation accelerators, and strategic alig
We introduce ASIA (Adaptive 3D Segmentation using few Image Annotations), a novel framework that enables segmentation of possibly non-semantic and non-text-describable "parts" in 3D. Our segmentation is controllable through a few user-annotated in-the-wild images, which are easier to collect than multi-view images, less demanding to annotate than 3D models, and more precise than potentially ambiguous text descriptions. Our method leverages the rich priors of text-to-image diffusion models, such as Stable Diffusion (SD), to transfer segmentations from image space to 3D, even when the annotated and target objects differ significantly in geometry or structure. During training, we optimize a text token for each segment and fine-tune our model with a novel cross-view part correspondence loss. At inference, we segment multi-view renderings of the 3D mesh, fuse the labels in UV-space via voting, refine them with our novel Noise Optimization technique, and finally map the UV-labels back onto the mesh. ASIA provides a practical and generalizable solution for both semantic and non-semantic 3D segmentation tasks, outperforming existing methods by a noticeable margin in both quantitative and q
Southeast Asia (SEA) is a region of extraordinary linguistic and cultural diversity, yet it remains significantly underrepresented in vision-language (VL) research. This often results in artificial intelligence (AI) models that fail to capture SEA cultural nuances. To fill this gap, we present SEA-VL, an open-source initiative dedicated to developing high-quality, culturally relevant data for SEA languages. By involving contributors from SEA countries, SEA-VL aims to ensure better cultural relevance and diversity, fostering greater inclusivity of underrepresented languages in VL research. Beyond crowdsourcing, our initiative goes one step further in the exploration of the automatic collection of culturally relevant images through crawling and image generation. First, we find that image crawling achieves approximately ~85% cultural relevance while being more cost- and time-efficient than crowdsourcing. Second, despite the substantial progress in generative vision models, synthetic images remain unreliable in accurately reflecting SEA cultures. The generated images often fail to reflect the nuanced traditions and cultural contexts of the region. Collectively, we gather 1.28M SEA cult
In this chapter, we investigate the spectrum measurement results in Asia-Pacific region. Then the spectrum sharing policy in the Asia-Pacific region is reviewed in details, where the national projects and strategies on spectrum refarming and spectrum sharing in China, Japan, Singapore, India, Korea and Australia are investigated. Then we introduce the spectrum sharing test-bed that is developed in China, which is a cognitive radio enabled TD-LTE test-bed utilizing TVWS. This chapter provides a brief introduction of the spectrum sharing mechanism and policy of Asia-Pacific region.
This study constructs a fully data-driven and reproducible Southeast Asia Influence Index (SAII v3) to reduce bias from expert scoring and subjective weighting while mapping hierarchical power structures across the eleven ASEAN nations. We aggregate authoritative open-source indicators across four dimensions (economic, military, diplomatic, socio-technological) and apply a three-tiered standardization chain quantile-Box-Cox-min-max to mitigate outliers and skewness. Weights are obtained through equal-weight integration of Entropy Weighting Method (EWM), CRITIC, and PCA. Robustness is assessed via Kendall's tau, +/-20% weight perturbation, and 10,000 bootstrap iterations, with additional checks including +/-10% dimensional sensitivity and V2-V3 bump chart comparisons. Results show integrated weights: Economy 35-40%, Military 20-25%, Diplomacy about 20%, Socio-Technology about 15%. The regional landscape exhibits a one-strong, two-medium, three-stable, and multiple-weak pattern: Indonesia, Singapore, and Malaysia lead, while Thailand, the Philippines, and Vietnam form a mid-tier competitive band. V2 and V3 rankings are highly consistent (Kendall's tau = 0.818), though small mid-tier
Southeast Asia is a geopolitically and socio-economically significant region with unique challenges and opportunities. Intensifying progress in generative AI against a backdrop of existing health security threats makes applications of AI to mitigate such threats attractive but also risky if done without due caution. This paper provides a brief sketch of some of the applications of AI for health security and the regional policy and governance landscape. I focus on policy and governance activities of the Association of Southeast Asian Nations (ASEAN), an international body whose member states represent 691 million people. I conclude by identifying sustainability as an area of opportunity for policymakers and recommend priority areas for generative AI researchers to make the most impact with their work.
Work on bias in pretrained language models (PLMs) focuses on bias evaluation and mitigation and fails to tackle the question of bias attribution and explainability. We propose a novel metric, the $\textit{bias attribution score}$, which draws from information theory to measure token-level contributions to biased behavior in PLMs. We then demonstrate the utility of this metric by applying it on multilingual PLMs, including models from Southeast Asia which have not yet been thoroughly examined in bias evaluation literature. Our results confirm the presence of sexist and homophobic bias in Southeast Asian PLMs. Interpretability and semantic analyses also reveal that PLM bias is strongly induced by words relating to crime, intimate relationships, and helping among other discursive categories, suggesting that these are topics where PLMs strongly reproduce bias from pretraining data and where PLMs should be used with more caution.
As a tech company, Grab has expanded from transportation to food delivery, aiming to serve Southeast Asia with hyperlocalized applications. Information about places as transportation destinations can help to improve our knowledge about places as restaurants, so long as the spatial entity resolution problem between these datasets can be solved. In this project, we attempted to recognize identical place entities from databases of Points-of-Interest (POI) and GrabFood restaurants, using their spatial and textual attributes, i.e., latitude, longitude, place name, and street address. Distance metrics were calculated for these attributes and fed to tree-based classifiers. POI-restaurant matching was conducted separately for Singapore, Philippines, Indonesia, and Malaysia. Experimental estimates demonstrate that a matching POI can be found for over 35% of restaurants in these countries. As part of these estimates, test datasets were manually created, and RandomForest, AdaBoost, Gradient Boosting, and XGBoost perform well, with most accuracy, precision, and recall scores close to or higher than 90% for matched vs. unmatched classification. To the authors' knowledge, there are no previous p
We present here the East Asia to Italy Nearly Global VLBI (EATING VLBI) project. How this project started and the evolution of the international collaboration between Korean, Japanese, and Italian researchers to study compact sources with VLBI observations is reported. Problems related to the synchronization of the very different arrays and technical details of the telescopes involved are presented and discussed. The relatively high observation frequency (22 and 43 GHz) and the long baselines between Italy and East Asia produced high-resolution images. We present example images to demonstrate the typical performance of the EATING VLBI array. The results attracted international researchers and the collaboration is growing, now including Chinese and Russian stations. New in progress projects are discussed and future possibilities with a larger number of telescopes and a better frequency coverage are briefly discussed herein.
We examine whether harvest-time transitory shifts in employment and income lead to changes in political violence and social unrest in rice-producing croplands of Southeast Asia. Using monthly data from 2010 to 2023 on over 86,000 incidents covering 376 one-degree cells across eight Southeast Asian countries, we estimate a general increase in political violence and a decrease in social unrest in croplands with rice production during the harvest season relative to the rest of the crop year. In a finding that is least sensitive to alternative model specifications and data subsetting, we estimate a nine percent increase in violence against civilians in locations with considerable rice production compared to other parts of the region during the harvest season, relative to the rest of the year. We show that the harvest-time changes in conflict are most evident in rural cells with rainfed agriculture. Using location-specific annual variation in growing season rainfall, we then show that the harvest-time increase in violence against civilians occurs in presumably good harvest years, whereas increase in battles between actors of political violence follows growing seasons with scarce rainfal
We show that the epidemiological Renormalization Group (eRG) framework is a useful and minimal tool to effectively describe the temporal evolution of the Dengue multi-wave pandemics. We test the framework on the Dengue history of several countries located in both Latin America and Asia. We also observe a strong correlation between the total number of infected individuals and the changes in the local temperature. Our results further support the expectation that global warming is bound to increase the cases of Dengue worldwide. We then move to investigate, via the eRG, the recent outbreak in Fano, Italy and offer our projections.