7 papers
How AI Assistance Affects Human Skill Development: A Study of Learning with Logic Puzzles
Shang Wu, Catarina G Belem, Shuyuan Fu +2
While AI assistance can improve human task performance in the short term, it may also undermine the development of skills in the longer term. We examine this tension in a controlle…
Learning to Assign Prediction Tasks to Agents with Capacity Constraints
Shang Wu, Saatvik Kher, Padhraic Smyth
We address the problem of learning to assign prediction tasks to one agent from a set of available agents, including human decision-makers and AI models. We focus on sequential lea…
AI evaluation may bias perceptions: The importance of context in interpreting academic writing
Shang Wu, Randol Yao
This paper examines how estimates of AI use in scientific writing can be biased when evaluation methods ignore contextual differences across countries and fields. Using large-scale…
The Impact of AI Usage and Informativeness on Skill Development in Logical Reasoning
Shang Wu, Hongyu Yao, Catarina Belem +3
Artificial intelligence (AI) is being increasingly integrated into human problem-solving, yet its effects on individual skill development remain unclear. We examine how both AI usa…
Scaling Teams or Scaling Time? Memory Enabled Lifelong Learning in LLM Multi-Agent Systems
Shanglin Wu, Yuyang Luo, Yueqing Liang +4
Large language model (LLM) multi-agent systems can scale along two distinct dimensions: by increasing the number of agents and by improving through accumulated experience over time…
A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents
Wei-Chieh Huang, Weizhi Zhang, Yueqing Liang +56
Research in artificial intelligence is shifting from model innovations and benchmark scores towards problem definition and rigorous real-world evaluation. As the field enters the "…
Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction
Shanglin Wu, Lihui Liu, Jinho D. Choi +1
Large Language Models (LLMs) often struggle with producing factually consistent answers due to limitations in their parametric memory. Retrieval-Augmented Generation (RAG) paradigm…