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cs.LG2026
Efficient Omni-modal Large Language Model Inference via Per-Query Modality Selection
Shuqi Liu, Yuzhou Cao, Luke Ong
Omni-modal large language models (Omni-LLMs) can jointly process a broad range of modalities, such as text, audio, and video, enabling stronger multimodal understanding and reasoni…
cs.CL2026
Estimating Uncertainty of Omnimodal Large Language Models via External Omnimodal Embeddings
Iacopo Ghinassi, Hy Nguyen Gia La, Luke Ong
Recent work has investigated Omni-modal Large Language Models (OLLMs), models which can take multiple modalities and their combinations as input, as a way forward to have systems r…
cs.CY2026
The 2026 Singapore Consensus on Global AI Safety Research Priorities
Stephen Casper, Oskar Galeev, Yoshua Bengio +117
Frontier AI capabilities and autonomy are advancing rapidly. A growing number of real-world incidents make a trusted AI ecosystem essential to embracing AI with confidence. The 202…