SCBX Group has reinforced its position as a leading financial technology group, advancing its commitment to frontier research with global recognition. Most recently, AI researchers across the SCBX group have achieved another milestone, with five research papers accepted across four leading global academic conferences—namely ACL 2026 (Main Conference), EACL 2026 (Main Conference), the ICLR 2026 Workshop on Principled Design for Trustworthy AI, and the ICLR 2026 Blogposts Track. These forums are widely regarded as premier venues in the global AI research ecosystem, where leading technology companies—including Google, Microsoft, Amazon, and Apple—regularly publish cutting-edge work. The achievement underscores both the depth and breadth of SCBX’s research capabilities, as well as the growing international presence of Thai AI talent.
This achievement reflects close collaboration across research teams within the SCBX group, including SCBX and SCB DataX. The joint effort focuses on advancing AI across three strategic research pillars that address both the specific needs of Thai users and the broader advancement of global AI knowledge:
Enhancing performance and safety of AI in Thai language and cultural contexts,
Developing audio-language capabilities for real-world applications, and
Deepening foundational research in reasoning for large language models (LLMs).
The Association for Computational Linguistics (ACL) and its European chapter, EACL, are globally recognized as leading venues in Natural Language Processing (NLP) — the field underpinning systems such as ChatGPT, Gemini, and Claude. Meanwhile, the International Conference on Learning Representations (ICLR) remains a premier forum for breakthroughs in machine learning and deep learning. Its Blogposts Track serves as a selective channel for disseminating high-impact technical insights to the global research community.
Mr. Kaweewut Temphuwapat, Chief Innovation Officer of SCBX and Chief Executive Officer of SCB 10X, said “We are honored that our research has been accepted at leading global conferences including ACL, EACL, and ICLR—forums known for their rigorous peer review and high standards. This milestone reflects SCBX’s continued commitment to advancing frontier research at the highest level.”
“These five papers highlight our approach to AI development—one that goes beyond model capability to address three critical dimensions: real-world usability in Thai language contexts, system-level safety and trust, and deep investment in foundational research. These elements are essential for deploying AI in high-trust environments such as financial services.” Kaweewut added.
The achievement reinforces SCBX’s long-term strategy of investing in frontier research to generate knowledge and innovation with real-world applicability. At the same time, the group continues to promote an open research approach, fostering collaboration across academia and industry to accelerate capability-building within Thailand’s AI ecosystem.
Collectively, these research contributions mark a further step toward positioning Thailand as a regional hub for AI innovation, advancing technologies that are locally relevant, safe, and globally impactful.
Five SCBX Research Contributions on the Global Stage:
Language-Aware Token Boosting (LATB)
The paper accepted to ACL 2026 (Main Conference) introduces a novel technique, Language-Aware Token Boosting (LATB), designed to address a common challenge in Thai-language LLM usage—language drift, where models respond in English or mix languages despite prompts being in Thai, resulting in an unnatural user experience.
LATB significantly mitigates this issue without requiring additional model fine-tuning, reducing both computational cost and development time, while enabling more efficient real-world deployment and a more consistent Thai-language user experience.
ThaiSafetyBench: Benchmarking AI Safety in Thai Contexts
The paper accepted to the ICLR 2026 Workshop, Principled Design for Trustworthy AI, introduces ThaiSafetyBench, a safety benchmark for large language models (LLMs) specifically designed for Thai language and cultural contexts.
Global AI safety evaluations today remain heavily reliant on English-centric benchmarks, leaving context-specific risks—such as those related to Thai social norms, cultural nuances, and local values—largely unexamined. As a result, organizations in Thailand lack standardized tools to assess whether AI systems are sufficiently safe for real-world deployment in Thai contexts.
ThaiSafetyBench addresses this gap with a dataset of 1,954 Thai-language test samples, covering six risk categories and 17 harm types. The research team evaluated more than 24 leading AI models, including Claude Sonnet 4.5, GPT-5, Gemini, Llama, Gemma, and Qwen, as well as locally developed models such as Typhoon and OpenThaiGPT.
The study finds that culturally contextualized attacks achieve significantly higher success rates than generic attacks, highlighting critical vulnerabilities in current AI systems that remain unresolved at the global level.
To support broader ecosystem development, the team has released the dataset, leaderboard, and a harmful content detection tool, ThaiSafetyClassifier, as open-source resources, enabling researchers and developers across Thailand to advance AI safety standards collaboratively.
AudioJudge: Unified Audio Evaluation Using Large Audio Models
The paper accepted to EACL 2026 (Main Conference), led by the Typhoon team at SCB DataX, introduces AudioJudge, a framework that leverages large audio models (LAMs) as unified evaluators to assess multiple dimensions of speech simultaneously—including pronunciation, speaking rate, speaker identification, and audio quality—replacing the need for separate specialized systems.
The proposed multi-aspect ensemble AudioJudge achieves a Spearman correlation of up to 0.91, closely aligning with human judgment, marking a significant step toward developing evaluation systems that more accurately reflect human perception.
Extending Audio Context for Long-Form Understanding
Another paper from the Typhoon team (SCB DataX), accepted to EACL 2026 (Main Conference), tackles a key bottleneck in large audio-language models (LALMs), which are typically constrained by short audio input limits despite longer text context capabilities. The study introduces Partial YaRN, a modality-decoupled method for extending audio context without affecting text performance, alongside Virtual Longform Audio Training (VLAT), enabling models to generalize effectively to longer audio sequences. These advancements pave the way for real-world applications requiring long-form audio understanding, including meetings, call center operations, and large-scale audio content processing.
Wait, Do We Need to Wait? Revisiting Budget Forcing for Sequential Test-Time Scaling
The paper accepted to the ICLR 2026 Blogposts Track re-examines Budget Forcing, a technique for enhancing LLM reasoning by controlling “thinking budgets” and prompting continued reasoning with cues such as “Wait.” Through systematic evaluation across multiple model families—including Qwen, Llama, Gemma, and Mistral—the study finds that performance gains are non-linear, challenging prior assumptions, and that “Wait” is not consistently the most effective trigger. Instead, naturally frequent tokens such as “Let” or “Perhaps” often yield better results. The research provides practical guidelines for applying test-time scaling and offers new insights to the global AI research community.
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