5 citations · 11 across the 14 of their papers we have counts for
15 papers
CVIF: A Criticality-Driven Visual Intervention Framework for Geometric Diagram Understanding in MLLMs
Jiahui Kang, Bifan Wei, Lingling Zhang +4
Despite significant progress in visual tasks by Multimodal Large Language Models (MLLMs), geometric diagram understanding remains challenging due to the presence of sparse visual c…
Dual-Cluster Memory Agent: Resolving Multi-Paradigm Ambiguity in Optimization Problem Solving
Xinyu Zhang, Yuchen Wan, Boxuan Zhang +4
Large Language Models (LLMs) often struggle with structural ambiguity in optimization problems, where a single problem admits multiple related but conflicting modeling paradigms, h…
Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems
Shihao Qi, Jie Ma, Rui Xing +15
LLM-based autonomous agents have demonstrated strong capabilities in reasoning, planning, and tool use, yet remain limited when tasks require sustained coordination across roles, t…
OptiVerse: A Comprehensive Benchmark towards Optimization Problem Solving
Xinyu Zhang, Boxuan Zhang, Yuchen Wan +5
While Large Language Models (LLMs) demonstrate remarkable reasoning, complex optimization tasks remain challenging, requiring domain knowledge and robust implementation. However, e…
$\textbf{AGT$^{AO}$}$: Robust and Stabilized LLM Unlearning via Adversarial Gating Training with Adaptive Orthogonality
Pengyu Li, Lingling Zhang, Zhitao Gao +5
While Large Language Models (LLMs) have achieved remarkable capabilities, they unintentionally memorize sensitive data, posing critical privacy and security risks. Machine unlearni…
ErrEval: Error-Aware Evaluation for Question Generation through Explicit Diagnostics
Weiping Fu, Bifan Wei, Jingyi Hao +7
Automatic Question Generation (QG) often produces outputs with critical defects, such as factual hallucinations and answer mismatches. However, existing evaluation methods, includi…
From Static to Dynamic: Adaptive Monte Carlo Search for Mathematical Process Supervision
Jie Ma, Shihao Qi, Rui Xing +4
The quality of process data plays a key role in training a Process Reward Model (PRM), which can enhance the complex mathematical reasoning capability of large language models. Exi…
GKG-LLM: A Unified Framework for Generalized Knowledge Graph Construction
Jian Zhang, Bifan Wei, Shihao Qi +3
The construction of Generalized Knowledge Graph (GKG), including knowledge graph, event knowledge graph and commonsense knowledge graph, is fundamental for various natural language…
Hierarchical Alignment-enhanced Adaptive Grounding Network for Generalized Referring Expression Comprehension
Yaxian Wang, Henghui Ding, Shuting He +3
In this work, we address the challenging task of Generalized Referring Expression Comprehension (GREC). Compared to the classic Referring Expression Comprehension (REC) that focuse…
QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation
Weiping Fu, Bifan Wei, Jianxiang Hu +2
Automatically generated questions often suffer from problems such as unclear expression or factual inaccuracies, requiring a reliable and comprehensive evaluation of their quality.…
Knowledge forest: a novel model to organize knowledge fragments
Qinghua Zheng, Jun Liu, Hongwei Zeng +3
With the rapid growth of knowledge, it shows a steady trend of knowledge fragmentization. Knowledge fragmentization manifests as that the knowledge related to a specific topic in a…
Extended Answer and Uncertainty Aware Neural Question Generation
Hongwei Zeng, Zhuo Zhi, Jun Liu +1
In this paper, we study automatic question generation, the task of creating questions from corresponding text passages where some certain spans of the text can serve as the answers…
Exploiting Source-Object Network to Resolve Object Conflicts in Linked Data
Wenqiang Liu, Jun Liu, Haimeng Duan +2
Considerable effort has been made to increase the scale of Linked Data. However, an inevitable problem when dealing with data integration from multiple sources is that multiple dif…
TruthDiscover: Resolving Object Conflicts on Massive Linked Data
Wenqiang Liu, Jun Liu, Jian Zhang +2
Considerable effort has been made to increase the scale of Linked Data. However, because of the openness of the Semantic Web and the ease of extracting Linked Data from semi-struct…
Truth Discovery to Resolve Object Conflicts in Linked Data
Wenqiang Liu
In the community of Linked Data, anyone can publish their data as Linked Data on the web because of the openness of the Semantic Web. As such, RDF (Resource Description Framework)…