12 papers
MMErroR: A Benchmark for Erroneous Reasoning in Vision-Language Models
Yang Shi, Yifeng Xie, Minzhe Guo +6
Recent advances in Vision-Language Models (VLMs) have improved performance in multi-modal learning, raising the question of whether these models truly understand the content they p…
AMA: Adaptive Memory via Multi-Agent Collaboration
Weiquan Huang, Zixuan Wang, Hehai Lin +6
The rapid evolution of Large Language Model (LLM) agents has necessitated robust memory systems to support cohesive long-term interaction and complex reasoning. Benefiting from the…
EchoTrail-GUI: Building Actionable Memory for GUI Agents via Critic-Guided Self-Exploration
Runze Li, Yuwen Zhai, Bo Xu +5
Contemporary GUI agents, while increasingly capable due to advances in Large Vision-Language Models (VLMs), often operate with a critical limitation: they treat each task in isolat…
Self-Guided Function Calling in Large Language Models via Stepwise Experience Recall
Sijia Cui, Aiyao He, Shuai Xu +5
Function calling enables large language models (LLMs) to interact with external systems by leveraging tools and APIs. When faced with multi-step tool usage, LLMs still struggle wit…
Coarse-to-Fine Grounded Memory for LLM Agent Planning
Wei Yang, Jinwei Xiao, Hongming Zhang +3
Recent advancements in Large Language Models (LLMs) have driven growing interest in LLM-based agents for complex planning tasks. To avoid costly agent training, many studies adopte…
An Anatomy of Vision-Language-Action Models: From Modules to Milestones and Challenges
Chao Xu, Suyu Zhang, Yang Liu +11
Vision-Language-Action (VLA) models are driving a revolution in robotics, enabling machines to understand instructions and interact with the physical world. This field is exploding…