STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning (2026 · 3 citations)
LLMs are increasingly applied to time-series domains. Yet settings such as traffic networks, power grids or epidemic spread need more than point forecasts. Decision-makers want to know the cause, location and timing of events, which means linking signals across graph-connected nodes while accounting for propagation…
Can LLM Agents Simulate Multi-Turn Human Behavior? Evidence from Real Online Customer Behavior Data (2026 · 2 citations)
Studies of LLM agents that imitate people have mostly asked whether their behavior looks believable to human judges.
Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations (2026)
This survey asks how graph structures can offset three built-in weaknesses of large language models. The first is knowledge: static training leads to outdated facts, errors, and unwanted memorization. The second is reasoning: token-by-token generation produces linear chains that cannot branch or backtrack. The third…

Co-authors here

12 of 16 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 4 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated 1 November 2026