3 citations · 3 across the 14 of their papers we have counts for
6 papers · 1 filter
Toward Knowledge-Centric AI: Discovering, Internalizing, and Using Explicit Knowledge Across Agents and Generations
Yisen Gao, Haoyu Huang, Zhongwei Xie +7
Knowledge is how understanding accumulates across people and generations: durable records that outlive their producers and that others can check, combine, teach, and correct. AI ag…
KGPFN: Unlocking the Potential of Knowledge Graph Foundation Model via In-Context Learning
Yisen Gao, Jiaxin Bai, Haoyu Huang +5
Knowledge graph (KG) foundation models aim to generalize to graphs with unseen entities and relations by learning transferable relational structure. Most existing methods, however,…
The Cognitive Bandwidth Bottleneck: Shifting Long-Horizon Agent from Planning with Actions to Planning with Schemas
Baixuan Xu, Tianshi Zheng, Zhaowei Wang +4
Enabling LLMs to effectively operate long-horizon task which requires long-term planning and multiple interactions is essential for open-world autonomy. Conventional methods adopt…
NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
Tianshi Zheng, Kelvin Kiu-Wai Tam, Newt Hue-Nam K. Nguyen +10
Large language models are emerging as powerful tools for scientific law discovery, a foundational challenge in AI-driven science. However, existing benchmarks for this task suffer…
TERAG: Token-Efficient Graph-Based Retrieval-Augmented Generation
Qiao Xiao, Hong Ting Tsang, Jiaxin Bai
Graph-based Retrieval-augmented generation (RAG) has become a widely studied approach for improving the reasoning, accuracy, and factuality of Large Language Models (LLMs). However…
Top Ten Challenges Towards Agentic Neural Graph Databases
Jiaxin Bai, Zihao Wang, Yukun Zhou +16
Graph databases (GDBs) like Neo4j and TigerGraph excel at handling interconnected data but lack advanced inference capabilities. Neural Graph Databases (NGDBs) address this by inte…