most citedWhen Your Reviewer is an LLM: Biases, Divergence, and Prompt Injection Risks in Peer Review

1 citations · 2 across the 3 of their papers we have counts for

collaborators

6 papers

cs.RO2026

Whether We Care, How We Reason: The Dual Role of Anthropomorphism and Moral Foundations in Robot Abuse

Fan Yang, Renkai Ma, Yaxin Hu +1

As robots become increasingly integrated into daily life, understanding responses to robot mistreatment carries important ethical and design implications. This mixed-methods study…

cs.CY2025

LLM Use for Mental Health: Crowdsourcing Users' Sentiment-based Perspectives and Values from Social Discussions

Lingyao Li, Xiaoshan Huang, Renkai Ma +4

Large language models (LLMs) chatbots like ChatGPT are increasingly used for mental health support. They offer accessible, therapeutic support but also raise concerns about misinfo…

cs.RO2025

Beyond the Uncanny Valley: A Mixed-Method Investigation of Anthropomorphism in Protective Responses to Robot Abuse

Fan Yang, Lingyao Li, Yaxin Hu +2

Robots with anthropomorphic features are increasingly shaping how humans perceive and morally engage with them. Our research investigates how different levels of anthropomorphism i…

cs.RO2025

I don't Want You to Die: A Shared Responsibility Framework for Safeguarding Child-Robot Companionship

Fan Yang, Renkai Ma, Yaxin Hu +2

Social robots like Moxie are designed to form strong emotional bonds with children, but their abrupt discontinuation can cause significant struggles and distress to children. When…

cs.CY20251 cited

When Your Reviewer is an LLM: Biases, Divergence, and Prompt Injection Risks in Peer Review

Changjia Zhu, Junjie Xiong, Renkai Ma +3

Peer review is the cornerstone of academic publishing, yet the process is increasingly strained by rising submission volumes, reviewer overload, and expertise mismatches. Large lan…

cs.CY20251 cited

Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs "In the Wild"

Lingyao Li, Renkai Ma, Zhaoqian Xue +1

While Large Language Models (LLMs) are rapidly integrating into daily life, research on their risks often remains lab-based and disconnected from the problems users encounter "in t…