activity
20242026
most citedSTBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Evolutionary Safety of Recursive Self-Improving AI: Taxonomy, Risk Discovery, and Evaluation

Chang Gong, Jingping Bi, Di Yao +6

Rapid advances in AI are bringing recursive self-improvement (RSI) closer to practice, with growing progress in model self-training, self-evolving agents, and automated AI developm…

cs.AI2025

GTool: Graph Enhanced Tool Planning with Large Language Model

Wenjie Chen, Wenbin Li, Di Yao +3

Tool planning with large language models (LLMs), referring to selecting, organizing, and preparing the tools necessary to complete a user request, bridges the gap between natural l…

cs.AI2024

PORCA: Root Cause Analysis with Partially Observed Data

Chang Gong, Di Yao, Jin Wang +6

Root Cause Analysis (RCA) aims at identifying the underlying causes of system faults by uncovering and analyzing the causal structure from complex systems. It has been widely used…

cs.CL2024★ 2 cited

STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis

Wenbin Li, Di Yao, Ruibo Zhao +7

The rapid evolution of large language models (LLMs) holds promise for reforming the methodology of spatio-temporal data mining. However, current works for evaluating the spatio-tem…

cs.LG2024

AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models

Shuo Liu, Di Yao, Lanting Fang +5

Detecting anomaly edges for dynamic graphs aims to identify edges significantly deviating from the normal pattern and can be applied in various domains, such as cybersecurity, fina…