COHERENCE is a new benchmark for measuring MLLMs' ability to recover fine-grained image-text correspondences in interleaved multimodal contexts.
MMIE: massive multimodal interleaved comprehension benchmark for large vision-language models
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
roles
dataset 1polarities
use dataset 1representative citing papers
LingoLoop traps MLLMs into generating up to 367 times more tokens by applying POS-aware attention adjustments to postpone EOS tokens and pruning generative paths to sustain repetitive loops.
Emu3.5 is a native multimodal world model pre-trained on over 10 trillion vision-language tokens with next-token prediction, post-trained via reinforcement learning, and accelerated by Discrete Diffusion Adaptation for efficient interleaved generation and world exploration.
citing papers explorer
-
COHERENCE: Benchmarking Fine-Grained Image-Text Alignment in Interleaved Multimodal Contexts
COHERENCE is a new benchmark for measuring MLLMs' ability to recover fine-grained image-text correspondences in interleaved multimodal contexts.
-
LingoLoop Attack: Trapping MLLMs via Linguistic Context and State Entrapment into Endless Loops
LingoLoop traps MLLMs into generating up to 367 times more tokens by applying POS-aware attention adjustments to postpone EOS tokens and pruning generative paths to sustain repetitive loops.
-
Emu3.5: Native Multimodal Models are World Learners
Emu3.5 is a native multimodal world model pre-trained on over 10 trillion vision-language tokens with next-token prediction, post-trained via reinforcement learning, and accelerated by Discrete Diffusion Adaptation for efficient interleaved generation and world exploration.