4 citations · 4 across the 7 of their papers we have counts for
8 papers
Toward Effective and Reliable LLM Agents via Dynamic Ontology
Xiaohui Zhang, Zequn Sun, Chengyuan Yang +3
Large language model (LLM) agents rely heavily on knowledge encoded in model parameters or presented as unstructured context. In domain-specific tasks, this leaves important semant…
Intelligent Multimodal Retrieval and Reasoning for Geospatial Knowledge Discovery on the I-GUIDE Platform
Yunfan Kang, Erick Li, Furqan Baig +4
Geospatial knowledge discovery increasingly requires search across heterogeneous artifacts: datasets, maps, notebooks, software, publications, and the provenance links among them.…
EIBench: A Simulator-Based Benchmark and Turn-Credit RL for Emotion Management
Rongzhi Zhu, Xiang Huang, Yuchuan Wu +8
Emotional intelligence (EI) in Large Language Models (LLMs) is often evaluated through static understanding tasks or single-response dialogue generation. However, emotion managemen…
Harnessing Structural Context for Entity Alignment Foundation Models
Xingyu Chen, Yuanning Cui, Zequn Sun +1
Entity alignment (EA) aims to identify equivalent entities across heterogeneous knowledge graphs (KGs) and is a key component of knowledge fusion and cross-KG reasoning. The recent…
Breaking the Reasoning Horizon in Entity Alignment Foundation Models
Yuanning Cui, Zequn Sun, Wei Hu +2
Entity alignment (EA) is critical for knowledge graph (KG) fusion. Existing EA models lack transferability and are incapable of aligning unseen KGs without retraining. While using…
KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering
Yuanning Cui, Zequn Sun, Wei Hu +1
Large language models (LLMs) excel at reasoning but struggle with knowledge-intensive questions due to limited context and parametric knowledge. However, existing methods that rely…
A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning
Yuanning Cui, Zequn Sun, Wei Hu
Extensive knowledge graphs (KGs) have been constructed to facilitate knowledge-driven tasks across various scenarios. However, existing work usually develops separate reasoning mod…
Expanding the Scope: Inductive Knowledge Graph Reasoning with Multi-Starting Progressive Propagation
Zhoutian Shao, Yuanning Cui, Wei Hu
Knowledge graphs (KGs) are widely acknowledged as incomplete, and new entities are constantly emerging in the real world. Inductive KG reasoning aims to predict missing facts for t…