7 citations · 7 across the 5 of their papers we have counts for
10 papers
Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory
Minghao Guo, Qingyue Jiao, Xiujin Liu +77
Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities…
RecToolBench: Benchmarking Recommendation-Specific Tool Orchestration under Fuzzy User Intent
Xiao Chen, Yicheng Zhao, Yingying Wu +4
Recent advances in agentic recommender systems are shifting recommender systems from passive filtering engines to instruction-following agents that use external tools to resolve us…
SkillBrew: Multi-Objective Curation of Skill Banks for LLM Agents
Wentao Hu, Zhendong Chu, Yiming Zhang +6
Retrieval-augmented LLM agents increasingly rely on curated skill banks: collections of reusable textual principles that guide decision making on complex tasks. Existing approaches…
ARM2: Adaptive Reasoning Model with Vision Understanding and Executable Code
Jian Xie, Zhendong Chu, Aoxiao Zhong +5
Large Reasoning Models (LRMs) often suffer from the ``over-thinking'' problem, generating unnecessarily long reasoning on simple tasks. Some strategies have been proposed to mitiga…
CircuitProbe: Tracing Visual Temporal Evidence Flow in Video Language Models
Yiming Zhang, Zhuokai Zhao, Chengzhang Yu +8
Autoregressive large vision--language models (LVLMs) interface video and language by projecting video features into the LLM's embedding space as continuous visual token embeddings.…
UniEDU: A Unified Language and Vision Assistant for Education Applications
Zhendong Chu, Jian Xie, Shen Wang +2
Education materials for K-12 students often consist of multiple modalities, such as text and images, posing challenges for models to fully understand nuanced information in these m…
LLM Agents for Education: Advances and Applications
Zhendong Chu, Shen Wang, Jian Xie +8
Large Language Model (LLM) agents are transforming education by automating complex pedagogical tasks and enhancing both teaching and learning processes. In this survey, we present…
Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning
Yibo Yan, Shen Wang, Jiahao Huo +7
Scientific reasoning, the process through which humans apply logic, evidence, and critical thinking to explore and interpret scientific phenomena, is essential in advancing knowled…
ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection
Yibo Yan, Shen Wang, Jiahao Huo +13
As the field of Multimodal Large Language Models (MLLMs) continues to evolve, their potential to revolutionize artificial intelligence is particularly promising, especially in addr…
AI-Driven Virtual Teacher for Enhanced Educational Efficiency: Leveraging Large Pretrain Models for Autonomous Error Analysis and Correction
Tianlong Xu, Yi-Fan Zhang, Zhendong Chu +2
Students frequently make mistakes while solving mathematical problems, and traditional error correction methods are both time-consuming and labor-intensive. This paper introduces a…