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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.CV2026
Correcting Guided Diffusion Trajectories with Spectral Alignment
Gihoon Kim, Taesup Kim
The practical success of conditional image generation hinges on fine-grained differences in condition alignment and visual fidelity. Classifier-free guidance (CFG) is central to th…
cs.AI2026
Cautious Context Steering for Language Model Personalization
Gihoon Kim, Jeyoung Lee, Suhan Woo +4
Personalizing language models (LMs) to individual user preferences is essential for aligning responses with diverse goals and backgrounds. Existing methods typically train a separa…