Long-context Multimodal Large Language Models (MLLMs) demand substantial
computational resources for inference as the growth of their multimodal Key-Value (KV) …

The rapid evolution of Multimodal Large Language Models (MLLMs) has brought substantial
advancements in artificial intelligence, significantly enhancing the capability to understand …

Vision language models (VLMs) are an exciting emerging class of language models (LMs)
that have merged classic LM capabilities with those of image processing systems. However …

While recent large language models (LLMs) demonstrate remarkable abilities in responding
to queries in diverse languages, their ability to handle long multilingual contexts is …

Vision-Language Models (VLMs) have shown promising capabilities in handling various
multimodal tasks, yet they struggle in long-context scenarios, particularly in tasks involving …

Current large multimodal models (LMMs) face significant challenges in processing and
comprehending long-duration or high-resolution videos, which is mainly due to the lack of …

We propose MMLU-SR, a novel dataset designed to measure the true comprehension
abilities of Large Language Models (LLMs) by challenging their performance in question …

Evaluating instruction following capabilities for multimodal, multi-turn dialogue is
challenging. With potentially multiple instructions in the input model context, the task is time …

Understanding information from a collection of multiple documents, particularly those with
visually rich elements, is important for document-grounded question answering. This paper …

Large Language Models (LLMs) have shown remarkable capabilities in code generation
tasks, yet they face significant limitations in handling complex, long-context programming …