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    <abstract>While AI development and evaluation for Southeast Asia (SEA) has grown rapidly, agent capabilities in regional languages are still poorly understood despite its importance to sovereign AI. To fill this gap, we introduce SEATauBench, the first agent-focused evaluation framework for SEA sovereign AI. It adapts Tau2-Bench to five languages—Mandarin, Vietnamese, Thai, Indonesian, and Filipino—and evaluates agents across progressively localized settings that vary the language of user-agent interaction, tool specifications, and task domains. Across three models, we find that English agent capabilities transfer reasonably well when only the conversation language changes, but quality and robustness degrade sharply as more task contexts are localized, with the largest losses in full domain adaptation. We also highlight the limits of English-only agent assessment for predicting agent capabilities in SEA languages. More broadly, SEATauBench provides a diagnostic benchmark and reusable adaptation pipeline for building reliable multilingual agents for linguistically diverse regions.</abstract>
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%0 Conference Proceedings
%T SEATauBench: Progressively Adapting Tool-Agent-User Evaluation Into Low-Resource Southeast Asian Languages
%A Nguyen, My Chiffon
%A Adila, Aulia
%A Ruangtanusak, Saksorn
%A Leesombatwathana, Kittiphat
%A Lim, Vissuta Gunawan
%A Payoungkhamdee, Patomporn
%A Cahyawijaya, Samuel
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F nguyen-etal-2026-seataubench
%X While AI development and evaluation for Southeast Asia (SEA) has grown rapidly, agent capabilities in regional languages are still poorly understood despite its importance to sovereign AI. To fill this gap, we introduce SEATauBench, the first agent-focused evaluation framework for SEA sovereign AI. It adapts Tau2-Bench to five languages—Mandarin, Vietnamese, Thai, Indonesian, and Filipino—and evaluates agents across progressively localized settings that vary the language of user-agent interaction, tool specifications, and task domains. Across three models, we find that English agent capabilities transfer reasonably well when only the conversation language changes, but quality and robustness degrade sharply as more task contexts are localized, with the largest losses in full domain adaptation. We also highlight the limits of English-only agent assessment for predicting agent capabilities in SEA languages. More broadly, SEATauBench provides a diagnostic benchmark and reusable adaptation pipeline for building reliable multilingual agents for linguistically diverse regions.
%U https://aclanthology.org/2026.findings-emnlp.502/
%P 10507-10543

Markdown (Informal)

[SEATauBench: Progressively Adapting Tool-Agent-User Evaluation Into Low-Resource Southeast Asian Languages](https://aclanthology.org/2026.findings-emnlp.502/) (Nguyen et al., Findings 2026)

• SEATauBench: Progressively Adapting Tool-Agent-User Evaluation Into Low-Resource Southeast Asian Languages (Nguyen et al., Findings 2026)

ACL

• My Chiffon Nguyen, Aulia Adila, Saksorn Ruangtanusak, Kittiphat Leesombatwathana, Vissuta Gunawan Lim, Patomporn Payoungkhamdee, and Samuel Cahyawijaya. 2026. SEATauBench: Progressively Adapting Tool-Agent-User Evaluation Into Low-Resource Southeast Asian Languages. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 10507–10543, Budapest, Hungary. Association for Computational Linguistics.