Yilun Hao

Parallelism Meets Adaptiveness: Scalable Documents Understanding in Multi-Agent LLM Systems

2025 · arXiv (Cornell University)

Large language model (LLM) agents have shown increasing promise for collaborative task completion. However, existing multi-agent frameworks often rely on static workflows, fixed roles, and limited inter-agent communication, reducing their effectiveness in open-ended, high-complexity domains. …

Code-as-Symbolic-Planner: Foundation Model-Based Robot Planning via Symbolic Code Generation

2025 · arXiv (Cornell University)

Recent works have shown great potentials of Large Language Models (LLMs) in robot task and motion planning (TAMP). Current LLM approaches generate text- or code-based reasoning chains with sub-goals and action plans. However, they do …