7 papers
SimpleMem: Efficient Lifelong Memory for LLM Agents
Jiaqi Liu, Yaofeng Su, Peng Xia +5
To support long-term interaction in complex environments, LLM agents require memory systems that manage historical experiences. Existing approaches either retain full interaction h…
Agent0-VL: Exploring Self-Evolving Agent for Tool-Integrated Vision-Language Reasoning
Jiaqi Liu, Kaiwen Xiong, Peng Xia +6
Vision-language agents have achieved remarkable progress in a variety of multimodal reasoning tasks; however, their learning remains constrained by the limitations of human-annotat…
GLIMPSE: Do Large Vision-Language Models Truly Think With Videos or Just Glimpse at Them?
Yiyang Zhou, Linjie Li, Shi Qiu +10
Existing video benchmarks often resemble image-based benchmarks, with question types like "What actions does the person perform throughout the video?" or "What color is the woman's…
ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding
Yiyang Zhou, Yangfan He, Yaofeng Su +5
Video understanding is fundamental to tasks such as action recognition, video reasoning, and robotic control. Early video understanding methods based on large vision-language model…
From EduVisBench to EduVisAgent: A Benchmark and Multi-Agent Framework for Reasoning-Driven Pedagogical Visualization
Haonian Ji, Shi Qiu, Siyang Xin +5
While foundation models (FMs), such as diffusion models and large vision-language models (LVLMs), have been widely applied in educational contexts, their ability to generate pedago…
MDocAgent: A Multi-Modal Multi-Agent Framework for Document Understanding
Siwei Han, Peng Xia, Ruiyi Zhang +4
Document Question Answering (DocQA) is a very common task. Existing methods using Large Language Models (LLMs) or Large Vision Language Models (LVLMs) and Retrieval Augmented Gener…