3 citations · 3 across the 6 of their papers we have counts for
7 papers
ValuePilot: A Two-Phase Framework for Value-Driven Decision-Making
Yitong Luo, Ziang Chen, Hou Hei Lam +4
Personalized decision-making is essential for human-AI interaction, enabling AI agents to act in alignment with individual users' value preferences. As AI systems expand into real-…
Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia
Chandler Smith, Marwa Abdulhai, Manfred Diaz +83
Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with bo…
ToMPO: Training LLM Strategic Decision Making from a Multi-Agent Perspective
Yiwen Zhang, Ziang Chen, Fanqi Kong +2
Large Language Models (LLMs) have been used to make decisions in complex scenarios, where they need models to think deeply, reason logically, and decide wisely. Many existing studi…
Aegis: Automated Error Generation and Attribution for Multi-Agent Systems
Fanqi Kong, Ruijie Zhang, Huaxiao Yin +7
Large language model based multi-agent systems (MAS) have unlocked significant advancements in tackling complex problems, but their increasing capability introduces a structural fr…
Can LLMs Generate Reliable Test Case Generators? A Study on Competition-Level Programming Problems
Yuhan Cao, Zian Chen, Kun Quan +17
Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, capable of tackling complex tasks during inference. However, the extent to which LLMs can…
ValuePilot: A Two-Phase Framework for Value-Driven Decision-Making
Yitong Luo, Hou Hei Lam, Ziang Chen +2
Despite recent advances in artificial intelligence (AI), it poses challenges to ensure personalized decision-making in tasks that are not considered in training datasets. To addres…
Enhancing Large-Scale AI Training Efficiency: The C4 Solution for Real-Time Anomaly Detection and Communication Optimization
Jianbo Dong, Bin Luo, Jun Zhang +22
The emergence of Large Language Models (LLMs) has necessitated the adoption of distributed training techniques, involving the deployment of thousands of GPUs to train a single mode…