31 citations · 78 across the 45 of their papers we have counts for
12 papers · 1 filter
JockeyBench: Evaluating LLM Agents on Horse-Racing Decision-Making
Zhongwei Xie, Xingsu Li, Jingqiao Yu +8
Repeated event forecasting requires extracting predictive signals from noisy evidence and evaluating their value across successive decisions. We introduce JockeyBench, a benchmark…
One Hypothesis Is Not Enough: Abductive Reasoning with Agentic Hypothesis Refinement over Knowledge Graphs
Yisen Gao, Yixi Cai, Tianshi Zheng +2
Abductive reasoning over knowledge graphs (KGs) seeks a first-order logic hypothesis whose answer set explains a given set of observed entities. Since many hypotheses can explain t…
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,…
DixitWorld: Evaluating Multimodal Abductive Reasoning in Vision-Language Models with Multi-Agent Dixit Gameplay
Yunxiang Mo, Tianshi Zheng, Qing Zong +6
Multimodal abductive reasoning--the generation and selection of explanatory hypotheses from partial observations--is a cornerstone of intelligence. Current evaluations of this abil…
Unifying Deductive and Abductive Reasoning in Knowledge Graphs with Masked Diffusion Model
Yisen Gao, Jiaxin Bai, Yi Huang +3
Deductive and abductive reasoning are two critical paradigms for analyzing knowledge graphs, enabling applications from financial query answering to scientific discovery. Deductive…
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…
Controllable Logical Hypothesis Generation for Abductive Reasoning in Knowledge Graphs
Yisen Gao, Jiaxin Bai, Tianshi Zheng +5
Abductive reasoning in knowledge graphs aims to generate plausible logical hypotheses from observed entities, with broad applications in areas such as clinical diagnosis and scient…
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…
Advancing Abductive Reasoning in Knowledge Graphs through Complex Logical Hypothesis Generation
Jiaxin Bai, Yicheng Wang, Tianshi Zheng +3
Abductive reasoning is the process of making educated guesses to provide explanations for observations. Although many applications require the use of knowledge for explanations, th…
Understanding Inter-Session Intentions via Complex Logical Reasoning
Jiaxin Bai, Chen Luo, Zheng Li +2
Understanding user intentions is essential for improving product recommendations, navigation suggestions, and query reformulations. However, user intentions can be intricate, invol…
Knowledge Graph Reasoning over Entities and Numerical Values
Jiaxin Bai, Chen Luo, Zheng Li +3
A complex logic query in a knowledge graph refers to a query expressed in logic form that conveys a complex meaning, such as where did the Canadian Turing award winner graduate fro…