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cs.AI2026
Safety for Whom? Benchmarking Understanding Beyond Refusal in Egocentric Multimodal Assistants
Yewon Han, Jisu Jeon, Suhyun Choi +5
Egocentric multimodal assistants see through the user's eyes and guide what the user does next, so their outputs directly shape physical actions, including those that affect bystan…
cs.CL2026
Compliance, Capability, and Conflict: Benchmarking Multimodal LLMs under System Messages
Juan Yeo, Geewook Kim
Production deployments of Multimodal Large Language Models (MLLMs) increasingly rely on system messages to govern model behavior. Yet existing benchmarks either evaluate constraint…
cs.LG2026
Stable On-Policy Distillation through Adaptive Target Reformulation
Ijun Jang, Jewon Yeom, Juan Yeo +2
Knowledge distillation (KD) is a widely adopted technique for transferring knowledge from large language models to smaller student models; however, conventional supervised KD often…