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统计口径与数据新鲜度

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新论文 · v1arXiv:2610.38181

Component Translation and Judge-Labelled Refusal: A Multilingual Safety Evaluation

Yongxi Zhou, Junwei Yao, Wenbo Ye, Jiahui Ge

原文摘要

Whole-prompt translation evaluates a request in different languages but does not compare which component carries a language. We introduce a paired reversal: translate the complete request component or the complete operation component into the same target language, holding behaviour, English frame and translated-span count fixed. Component identity and non-English text quantity change together. Assigning English, Chinese and Spanish independently to the two components yields nine configurations on 190 of 300 sampled harmful behaviours retained after consensus annotation and automatic gating. Refusal scored by language-model judges is the primary outcome; unsafe compliance is reported separately. On Gemini 2.5 Flash and DeepSeek, three refusal swaps are about 5.3 percentage points and the fourth is -0.5; none survives the pre-specified six-test Holm correction. In an exploratory extension, Mistral Small 4 has a 13.7-point Spanish swap (95% interval [8.4, 18.9]), which survives the twelve-test Holm sensitivity correction under the primary judge, and 12.1 points under a second judge. GPT-5.4-mini estimates are within 2 points of zero. Component assignment supplies an additional evaluation dimension. Evaluations should record component languages and translated text quantity, comparing matched behaviours at equal translated-span count while interpreting refusal and unsafe compliance separately.

cs.CL · cs.CR

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.428ACL Anthology · ACL 2026

Experience-driven Multi-turn Reinforcement Learning for GUI Agents

Zhengxi Lu, Jiabo Ye, Fei Tang Nankai University, Yongliang Shen Zhejiang University, Haiyang Xu, Ziwei Zheng Xi'an Jiaotong University, Weiming Lu Zhejiang University, Ming Yan Alibaba Group 等 11 位作者

原文摘要

GUI agents have demonstrated remarkable progress in automating complex user interface interactions. However, training such agents for long-horizon tasks remains challenging. Single-turn reinforcement learning conditions on expert histories during training but self-generated histories during deployment, causing distribution mismatch. Online multi-turn methods eliminate this gap via environment interaction but suffer from sparse rewards and prohibitive costs. We propose \underline{\textbf{E}}xperience-driven \underline{\textbf{M}}ulti-turn \underline{\textbf{P}}olicy \underline{\textbf{O}}ptimization (\textbf{EMPO}), which leverages expert trajectories as environment experiences for on-policy multi-turn training. The agent constructs self-generated history throughout rollouts; when actions match expert experiences, the trajectory provides valid state transitions, and a Patch Module recovers mismatched steps to maintain on-policy rollouts. EMPO further incorporates discounted future rewards and dual-level advantage estimation to capture long-horizon dependencies. We also propose \textbf{AndroidControl-Real}, an evaluation metric strongly correlated with real-world performance (R^2=0.934). With only 1K public trajectories as RL experiences, our method achieves substantial gains over the base model (e.g., +12.0% on AndroidWorld and +23.8% on AITW) and achieves competitive performance against strong baselines such as UI-TARS-7B and GPT-4o, demonstrating better generalization than prior single-turn RL approaches. Code available: https://anonymous.4open.science/r/UI-S1-0DAF.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表