8 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, VÃctor 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.…