4 citations · 6 across the 10 of their papers we have counts for
11 papers
Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale
Ang Li, Ben Liu, Bin Han +215
Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve,…
TKG-Thinker: Towards Dynamic Reasoning over Temporal Knowledge Graphs via Agentic Reinforcement Learning
Zihao Jiang, Miao Peng, Zhenyan Shan +5
Temporal knowledge graph question answering (TKGQA) aims to answer time-sensitive questions by leveraging temporal knowledge bases. While Large Language Models (LLMs) demonstrate s…
Plan Then Retrieve: Reinforcement Learning-Guided Complex Reasoning over Knowledge Graphs
Yanlin Song, Ben Liu, Víctor Gutiérrez-Basulto +5
Knowledge Graph Question Answering aims to answer natural language questions by reasoning over structured knowledge graphs. While large language models have advanced KGQA through t…
MoodAngels: A Retrieval-augmented Multi-agent Framework for Psychiatry Diagnosis
Mengxi Xiao, Ben Liu, He Li +5
The application of AI in psychiatric diagnosis faces significant challenges, including the subjective nature of mental health assessments, symptom overlap across disorders, and pri…
Towards Explainable Temporal Reasoning in Large Language Models: A Structure-Aware Generative Framework
Zihao Jiang, Ben Liu, Miao Peng +4
While large language models (LLMs) show great potential in temporal reasoning, most existing work focuses heavily on enhancing performance, often neglecting the explainable reasoni…
One Size doesn't Fit All: A Personalized Conversational Tutoring Agent for Mathematics Instruction
Ben Liu, Jihan Zhang, Fangquan Lin +2
Large language models (LLMs) have been increasingly employed in various intelligent educational systems, simulating human tutors to facilitate effective human-machine interaction.…
SymAgent: A Neural-Symbolic Self-Learning Agent Framework for Complex Reasoning over Knowledge Graphs
Ben Liu, Jihai Zhang, Fangquan Lin +3
Recent advancements have highlighted that Large Language Models (LLMs) are prone to hallucinations when solving complex reasoning problems, leading to erroneous results. To tackle…
Filter-then-Generate: Large Language Models with Structure-Text Adapter for Knowledge Graph Completion
Ben Liu, Jihai Zhang, Fangquan Lin +2
Large Language Models (LLMs) present massive inherent knowledge and superior semantic comprehension capability, which have revolutionized various tasks in natural language processi…
Deja vu: Contrastive Historical Modeling with Prefix-tuning for Temporal Knowledge Graph Reasoning
Miao Peng, Ben Liu, Wenjie Xu +3
Temporal Knowledge Graph Reasoning (TKGR) is the task of inferring missing facts for incomplete TKGs in complex scenarios (e.g., transductive and inductive settings), which has bee…
Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion
Wenjie Xu, Ben Liu, Miao Peng +2
Temporal Knowledge graph completion (TKGC) is a crucial task that involves reasoning at known timestamps to complete the missing part of facts and has attracted more and more atten…
SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Prediction
Miao Peng, Ben Liu, Qianqian Xie +3
Link prediction is the task of inferring missing links between entities in knowledge graphs. Embedding-based methods have shown effectiveness in addressing this problem by modeling…