3 papers
cs.AI2025
Agent2World: Learning to Generate Symbolic World Models via Adaptive Multi-Agent Feedback
Mengkang Hu, Bowei Xia, Yuran Wu +9
Symbolic world models (e.g., PDDL domains or executable simulators) are central to model-based planning, but training LLMs to generate such world models is limited by the lack of l…
cs.AI2025
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Mengkang Hu, Yuhang Zhou, Wendong Fan +13
Large Language Model (LLM)-based multi-agent systems show promise for automating real-world tasks but struggle to transfer across domains due to their domain-specific nature. Curre…
cs.AI2025
Truly Assessing Fluid Intelligence of Large Language Models through Dynamic Reasoning Evaluation
Yue Yang, MingKang Chen, Qihua Liu +9
Recent advances in large language models (LLMs) have demonstrated impressive reasoning capacities that mirror human-like thinking. However, whether LLMs possess genuine fluid intel…