26 citations · 28 across the 9 of their papers we have counts for
9 papers
Foundation Protocol: A Coordination Layer for Agentic Society
Bang Liu, Yongfeng Gu, Jiayi Zhang +26
Autonomous agents are moving from tools into a layer of social infrastructure: they browse, purchase, deploy software, manage systems, and increasingly interact with one another. A…
Mem-: Adaptive Memory through Learning When and What to Generate
Xiaoqiang Wang, Chao Wang, Hadi Nekoei +5
We present Mem-, a framework for adaptive memory in large language model (LLM) agents, where useful guidance is generated on demand rather than retrieved from external memory st…
OSCAR: Operating System Control via State-Aware Reasoning and Re-Planning
Xiaoqiang Wang, Bang Liu
Large language models (LLMs) and large multimodal models (LMMs) have shown great potential in automating complex tasks like web browsing and gaming. However, their ability to gener…
FACE: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition
Xiaoqiang Wang, Lingfei Wu, Tengfei Ma +1
Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. However, such a paradigm fails to comprehensively di…
SkillQG: Learning to Generate Question for Reading Comprehension Assessment
Xiaoqiang Wang, Bang Liu, Siliang Tang +1
We present $\textbf{$\texttt{SkillQG}$}$: a question generation framework with controllable comprehension types for assessing and improving machine reading comprehension models. Ex…
QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance
Xiaoqiang Wang, Bang Liu, Siliang Tang +1
Existing metrics for assessing question generation not only require costly human reference but also fail to take into account the input context of generation, rendering the lack of…
Feeding What You Need by Understanding What You Learned
Xiaoqiang Wang, Bang Liu, Fangli Xu +3
Machine Reading Comprehension (MRC) reveals the ability to understand a given text passage and answer questions based on it. Existing research works in MRC rely heavily on large-si…
Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images
Xiaoqiang Wang, Lei Zhu, Siliang Tang +5
Training deep models for RGB-D salient object detection (SOD) often requires a large number of labeled RGB-D images. However, RGB-D data is not easily acquired, which limits the de…
Hierarchical Graph Matching Network for Graph Similarity Computation
Haibo Xiu, Xiao Yan, Xiaoqiang Wang +2
Graph edit distance / similarity is widely used in many tasks, such as graph similarity search, binary function analysis, and graph clustering. However, computing the exact graph e…