Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities
in executing instructions for a variety of single-image tasks. Despite this progress, significant …

Multimodal Large Language Models (MLLMs) mimic human perception and reasoning
system by integrating powerful Large Language Models (LLMs) with various modality …

Multi-modal large language models (MLLMs) have demonstrated impressive performance in
vision-language tasks across a wide range of domains. However, the large model scale and …

Existing open-source multimodal large language models (MLLMs) generally follow a
training process involving pre-training and supervised fine-tuning. However, these models …

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

Large Multimodal Models (LMMs) have made significant strides in visual question-
answering for single images. Recent advancements like long-context LMMs have allowed …

Multimodal document understanding is a challenging task to process and comprehend large
amounts of textual and visual information. Recent advances in Large Language Models …

While many studies prove more advanced LLMs perform better on tasks such as math and
coding, we notice that in cryptocurrency trading, stronger LLMs work worse than weaker …

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 …