activity
20242026
most citedSYNERGAI: Perception Alignment for Human-Robot Collaboration

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Hypothesis Graph Refinement: Hypothesis-Driven Exploration with Cascade Error Correction for Embodied Navigation

Peixin Chen, Guoxi Zhang, Jianwei Ma +1

Embodied agents must explore partially observed environments while maintaining reliable long-horizon memory. Existing graph-based navigation systems improve scalability, but they o…

cs.AI2026

VISA: Value Injection via Shielded Adaptation for Personalized LLM Alignment

Jiawei Chen, Tianzhuo Yang, Guoxi Zhang +3

Aligning Large Language Models (LLMs) with nuanced human values remains a critical challenge, as existing methods like Reinforcement Learning from Human Feedback (RLHF) often handl…

cs.CV2026

MVR: Multi-view Video Reward Shaping for Reinforcement Learning

Lirui Luo, Guoxi Zhang, Hongming Xu +3

Reward design is of great importance for solving complex tasks with reinforcement learning. Recent studies have explored using image-text similarity produced by vision-language mod…

cs.AI2025

A Game-Theoretic Negotiation Framework for Cross-Cultural Consensus in LLMs

Guoxi Zhang, Jiawei Chen, Tianzhuo Yang +3

The increasing prevalence of large language models (LLMs) is influencing global value systems. However, these models frequently exhibit a pronounced WEIRD (Western, Educated, Indus…

cs.RO20241 cited

SYNERGAI: Perception Alignment for Human-Robot Collaboration

Yixin Chen, Guoxi Zhang, Yaowei Zhang +4

Recently, large language models (LLMs) have shown strong potential in facilitating human-robotic interaction and collaboration. However, existing LLM-based systems often overlook t…

cs.LG2024

VickreyFeedback: Cost-efficient Data Construction for Reinforcement Learning from Human Feedback

Guoxi Zhang, Jiuding Duan

This paper addresses the cost-efficiency aspect of Reinforcement Learning from Human Feedback (RLHF). RLHF leverages datasets of human preferences over outputs of large language mo…