5 papers
AMAP Agentic Planning Technical Report
AMAP AI Agent Team, Yulan Hu, Xiangwen Zhang +22
We present STAgent, an agentic large language model tailored for spatio-temporal understanding, designed to solve complex tasks such as constrained point-of-interest discovery and…
Detecting Unobserved Confounders: A Kernelized Regression Approach
Yikai Chen, Yunxin Mao, Chunyuan Zheng +7
Detecting unobserved confounders is crucial for reliable causal inference in observational studies. Existing methods require either linearity assumptions or multiple heterogeneous…
Environment Inference for Learning Generalizable Dynamical System
Shixuan Liu, Yue He, Haotian Wang +4
Data-driven methods offer efficient and robust solutions for analyzing complex dynamical systems but rely on the assumption of I.I.D. data, driving the development of generalizatio…
Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift
Shixuan Liu, Yue He, Yunfei Wang +5
Logical rule learning, a prominent category of knowledge graph (KG) reasoning methods, constitutes a critical research area aimed at learning explicit rules from observed facts to…
Enhancing Human Experience in Human-Agent Collaboration: A Human-Centered Modeling Approach Based on Positive Human Gain
Yiming Gao, Feiyu Liu, Liang Wang +12
Existing game AI research mainly focuses on enhancing agents' abilities to win games, but this does not inherently make humans have a better experience when collaborating with thes…