3 citations · 3 across the 3 of their papers we have counts for
5 papers
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…
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…
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…
Open Problems in Machine Unlearning for AI Safety
Fazl Barez, Tingchen Fu, Ameya Prabhu +16
As AI systems become more capable, widely deployed, and increasingly autonomous in critical areas such as cybersecurity, biological research, and healthcare, ensuring their safety…
Towards Interpreting Visual Information Processing in Vision-Language Models
Clement Neo, Luke Ong, Philip Torr +3
Vision-Language Models (VLMs) are powerful tools for processing and understanding text and images. We study the processing of visual tokens in the language model component of LLaVA…