SEATauBench: Adapting Tool-Agent-User Evaluation Into Low-Resource Southeast Asian Languages
arXiv:2606.28715v1 Announce Type: cross 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. SeaTau adapts TauBench 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.
The rapid growth of AI development and evaluation, particularly in the context of 'sovereign AI,' is driving the need for localized AI capabilities.
This initiative directly addresses the critical gap in agent capabilities for Southeast Asian languages, which is essential for sovereign AI ambitions and regional tech autonomy.
The availability of a standardized evaluation framework like SEATauBench will accelerate the development and performance of AI agents tailored for specific SEA linguistic and cultural contexts.
- · Southeast Asian governments
- · Local AI developers
- · Users in SEA languages
- · AI agent developers
- · Global AI models lacking localization
- · Dependence on non-native language data
Improved performance and broader adoption of AI agents in Southeast Asian languages for various applications.
Increased competition and innovation within the Southeast Asian AI ecosystem, potentially leading to regional AI champions.
Enhanced digital sovereignty and reduced reliance on foreign technology stacks for critical AI capabilities in SEA nations.
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Read at arXiv cs.AI