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From the 2 of 32 linked papers with an AI index.

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32 papers

cs.CL2026

Would You Walk to the Car Wash? Revealing the Salience Bias of Large Language Models in Commonsense Reasoning

Zheng Wu, Chenhao Xue, Shijie Zheng +3

The paper identifies a "salience bias" in large language models where explicit but irrelevant details cause the models to overlook implicit commonsense knowledge, and shows that th…

cs.CL2026

Beyond a Single Judge: The Evidence-Grounded, Social-Weighted Persona Panel for Generative UI Evaluation

Zheng Wu, Yibo Luo, Pu Zhang +2

The paper introduces ESPP, a three-stage evaluation framework that uses a panel of diverse, evidence‑grounded personas to rate generative UI screenshots, improving alignment with h…

cs.CL2026

Hidden Ghost Hand: Unveiling Backdoor Vulnerabilities in MLLM-Powered Mobile GUI Agents

Pengzhou Cheng, Haowen Hu, Zheng Wu +4

Graphical user interface (GUI) agents powered by multimodal large language models (MLLMs) have shown greater promise for human-interaction. However, due to the high fine-tuning cos…

cs.CL2026

MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft

Tianjie Ju, Yueqing Sun, Zheng Wu +7

Multimodal large language models (MLLMs) have shown strong capabilities in perception, reasoning, and action generation. However, their ability to sustain exploration in dynamic op…

cs.AI2026

EVA: Evolving Semantic Adversaries for Red-Teaming GUI Agents Against Environmental Injection Attacks

Yijie Lu, Manman Zhao, Tianjie Ju +6

Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) are increasingly deployed yet vulnerable to Environmental Injection Attacks (EIAs).However…

cs.CL2026

Mobile-Aptus: Confidence-Driven Proactive and Robust Interaction in MLLM-based Mobile-Using Agents

Zheng Wu, Pengzhou Cheng, Zongru Wu +5

Recent advancements in multimodal large language models (MLLMs) have shown exceptional potential in enabling mobile-using agents to autonomously execute human instructions. However…