4 citations · 5 across the 13 of their papers we have counts for
6 papers · 1 filter
Orca: The World is in Your Mind
Yihao Wang, Yuheng Ji, Mingyu Cao +54
We introduce Orca, an initial instantiation of a general world foundation model. Orca learns a unified world latent space from multimodal world signals and exposes it through multi…
Think While Watching: Online Streaming Segment-Level Memory for Multi-Turn Video Reasoning in Multimodal Large Language Models
Lu Wang, Zhuoran Jin, Yupu Hao +4
Multimodal large language models (MLLMs) have shown strong performance on offline video understanding, but most are limited to offline inference or have weak online reasoning, maki…
TrimTokenator-LC: Towards Adaptive Visual Token Pruning for Large Multimodal Models with Long Contexts
Hao Zhang, Mengsi Lyu, Bo Huang +2
Large Multimodal Models (LMMs) have proven effective on various tasks. They typically encode visual inputs into Original Model sequences of tokens, which are then concatenated with…
Emu3.5: Native Multimodal Models are World Learners
Yufeng Cui, Honghao Chen, Haoge Deng +20
We introduce Emu3.5, a large-scale multimodal world model that natively predicts the next state across vision and language. Emu3.5 is pre-trained end-to-end with a unified next-tok…
TrimTokenator: Towards Adaptive Visual Token Pruning for Large Multimodal Models
Hao Zhang, Mengsi Lyu, Chenrui He +2
Large Multimodal Models (LMMs) have achieved significant success across various tasks. These models usually encode visual inputs into dense token sequences, which are then concaten…
Emu3: Next-Token Prediction is All You Need
Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo +22
While next-token prediction is considered a promising path towards artificial general intelligence, it has struggled to excel in multimodal tasks, which are still dominated by diff…