most citedLanguage Shapes Mental Health Evaluations in Large Language Models

1 citations · 1 across the 8 of their papers we have counts for

collaborators

8 papers

cs.AI2026

WeClawArena: An Auditable Sandbox and Benchmark for Cross-User Agents Collaboration and Security in Human-Centered Agent Networks

Prince Zizhuang Wang, Aojie Yuan, Haiyue Zhang +3

Recent advances in persistent personal-agent frameworks are making human-centered agent networks realistic deployment targets: each user can be served by an AI agent that acts on t…

cs.CL2026

Lost in Execution: On the Multilingual Robustness of Tool Calling in Large Language Models

Zheng Luo, T Pranav Kutralingam, Ogochukwu N Okoani +3

Large Language Models (LLMs) are increasingly deployed as agents that invoke external tools through structured function calls. While recent work reports strong tool-calling perform…

cs.AI2026

FORTIS: Benchmarking Over-Privilege in Agent Skills

Shawn Li, Chenxiao Yu, Han Wang +8

Large language model agents increasingly operate through an intermediate skill layer that mediates between user intent and concrete task execution. This layer is widely treated as…

cs.CL20261 cited

Language Shapes Mental Health Evaluations in Large Language Models

Jiayi Xu, Xiyang Hu

Multilingual large language models (LLMs) are increasingly used in socially sensitive mental health contexts, including support chatbots, screening, and content moderation. This ra…

cs.AI2026

The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective

Xiaoou Liu, Tiejin Chen, Weibo Li +2

Foundation model agents are increasingly deployed for real-world decision-making, but suffer from the sim-to-real gap. While robotics and classical control have mature frameworks t…

cs.AI2026

Counterfactual Trace Auditing of LLM Agent Skills

Xiaolin Zhou, Jinbo Liu, Li Li +2

Large Language Model agents are increasingly augmented with agent skills. Current evaluation methods for skills remain limited. Most deployed benchmarks report only pass rate befor…