As a prominent direction of Artificial General Intelligence (AGI), Multimodal Large Language
Models (MLLMs) have garnered increased attention from both industry and academia …

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

Mobile agents are essential for automating tasks in complex and dynamic mobile
environments. As foundation models evolve, the demands for agents that can adapt in real …

We present MM1. 5, a new family of multimodal large language models (MLLMs) designed
to enhance capabilities in text-rich image understanding, visual referring and grounding …

Multimodal large language models (MLLMs) are transforming the capabilities of graphical
user interface (GUI) agents, facilitating their transition from controlled simulations to …

We introduce MIA-Bench, a new benchmark designed to evaluate multimodal large
language models (MLLMs) on their ability to strictly adhere to complex instructions. Our …

AI agents have drawn increasing attention mostly on their ability to perceive environments,
understand tasks, and autonomously achieve goals. To advance research on AI agents in …

Building a generalist model for user interface (UI) understanding is challenging due to
various foundational issues, such as platform diversity, resolution variation, and data …

Multipanel images, commonly seen as web screenshots, posters, etc., pervade our daily
lives. These images, characterized by their composition of multiple subfigures in distinct …

In this work, we propose a training-free method to inject visual referring into Multimodal
Large Language Models (MLLMs) through learnable visual token optimization. We observe …