most citedScreenLLM: Stateful Screen Schema for Efficient Action Understanding and Prediction

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Rubric-as-Experts: Case-Specific MQM Rubrics for Translation Error Span Detection

Weilu Xu, Yunzhi Shen, Xinye Wang +2

Large language models (LLMs) have shown potential for reference-free span-level translation quality estimation (QE), yet existing approaches based on Multidimensional Quality Metri…

cs.CL2026

PEGRL: Improving Machine Translation by Post-Editing Guided Reinforcement Learning

Yunzhi Shen, Hao Zhou, Xin Huang +3

Reinforcement learning (RL) has shown strong promise for LLM-based machine translation, with recent methods such as GRPO demonstrating notable gains; nevertheless, translation-orie…

cs.GR2026

Proc3D: Procedural 3D Generation and Parametric Editing of 3D Shapes with Large Language Models

Fadlullah Raji, Stefano Petrangeli, Matheus Gadelha +3

Generating 3D models has traditionally been a complex task requiring specialized expertise. While recent advances in generative AI have sought to automate this process, existing me…

cs.CL2025★ 2 cited

ScreenLLM: Stateful Screen Schema for Efficient Action Understanding and Prediction

Yiqiao Jin, Stefano Petrangeli, Yu Shen +1

Graphical User Interface (GUI) agents are autonomous systems that interpret and generate actions, enabling intelligent user assistance and automation. Effective training of these a…

cs.CL2025★ 1 cited

GUI-Bee: Align GUI Action Grounding to Novel Environments via Autonomous Exploration

Yue Fan, Handong Zhao, Ruiyi Zhang +3

Graphical User Interface (GUI) action grounding is a critical step in GUI automation that maps language instructions to actionable elements on GUI screens. Most recent works of GUI…