2 citations · 2 across the 2 of their papers we have counts for
5 papers
REINS: A Self-Evolving Agent Harness for Real-Time Trajectory Planning
Zhihong Cui, Hengyu Liu, Haoran Tang +3
Agent harnesses -- runtime scaffolds that wrap an LLM's reasoning loop with tool dispatch, verification, and persistent memory -- have become the standard substrate for deploying L…
Locating and Repairing Domain Shift in VLM Trajectory Planning
Zhihong Cui, Hengyu Liu, Michael Alexander Riegler +3
Vision-Language Models (VLMs) are increasingly adopted as end-to-end driving planners, yet they suffer from cross-city domain shift. Existing methods treat this shift as a single q…
GAPL: Grounded Action-effect Policy Learning for LLM-Based Trajectory Planning
Zhihong Cui, Hengyu Liu, Zhangkai Wu +5
Trajectory planning for autonomous driving requires both high-level reasoning and precise low-level control. Large Language Models (LLMs) offer semantic-rich planning capabilities,…
C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving
Zhihong Cui, Haoran Tang, Tianyi Li +4
Trajectory planning for autonomous driving increasingly leverages large language models (LLMs) for commonsense reasoning, yet LLM outputs are inherently unreliable, posing risks in…
Using LLM-Generated Draft Replies to Support Human Experts in Responding to Stakeholder Inquiries in Maritime Industry: A Real-World Case Study of Industrial AI
Tita Alissa Bach, Aleksandar Babic, Narae Park +4
The maritime industry requires effective communication among diverse stakeholders to address complex, safety-critical challenges. Industrial AI, including Large Language Models (LL…