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cs.AI2026
Automated Analysis of Global AI Safety Initiatives: A Taxonomy-Driven LLM Approach
Takayuki Semitsu, Naoto Kiribuchi, Kengo Zenitani
We present an automated crosswalk framework that compares an AI safety policy document pair under a shared taxonomy of activities. Using the activity categories defined in Activity…
cs.AI2026
Improving Methodologies for LLM Evaluations Across Global Languages
Akriti Vij, Benjamin Chua, Darshini Ramiah +43
As frontier AI models are deployed globally, it is essential that their behaviour remains safe and reliable across diverse linguistic and cultural contexts. To examine how current…
cs.AI2026
Improving Methodologies for Agentic Evaluations Across Domains: Leakage of Sensitive Information, Fraud and Cybersecurity Threats
Ee Wei Seah, Yongsen Zheng, Naga Nikshith +67
The rapid rise of autonomous AI systems and advancements in agent capabilities are introducing new risks due to reduced oversight of real-world interactions. Yet agent testing rema…