Automating Graphical User Interface (GUI) operations with Multimodal Large Language Models (MLLMs) is promising but remains bottlenecked in real-world long-horizon settings. Key challenges include ensuring precise grounding across diverse interfaces and handling irreversible errors in extended workflows. Current methods often struggle to distinguish targets in low Signal-to-Noise Ratio (SNR) environments and lack sufficient pre-execution verification to prevent error accumulation. To address this, we propose the Memory-augmented Debate System (MaDS). Specifically, MaDS combines: (1) a Dual-Layer Memory Module that integrates universal interaction priors with scenario-specific operational experience to mitigate grounding hallucinations; and (2) Multi-Round Debate that performs pre-execution verification, while transforming execution failures into retrievable Negative Warnings to reduce repeated errors. Additionally, we introduce MaDS-Benchmark, a benchmark for long-horizon mobile GUI tasks with process-oriented evaluation. Experiments show that MaDS achieves a 90.23% Task Success Rate on MaDS-Benchmark and strong performance on public benchmarks including AITW, AITZ, CAGUI, and GUIOdyssey.

Fine-grained hand interaction with Inertial Measurement Unit (IMU) and machine learning offers a low-cost and effective solution. However, the robustness and generalizability of machine learning models are highly dataset-dependent. Existing datasets for interaction design are typically constructed through extensive real user data collection, which limits interaction diversity and personalization. To address these challenges, we propose Rob2HanD, a novel data-generation tool which utilizes large language models (LLMs) to regulate the motion processes of the robotic arm and rapidly constructs IMU datasets. Rob2HanD demonstrates the capability to generate large and usable IMU interaction datasets under few-shot or zero-shot conditions, thereby enhancing the potential for diverse and personalized fine-grained hand interactions. Using a real human dataset, we evaluate machine learning models trained on Rob2HanD-generated data and validate the usability of Rob2HanD. In real-world applications, models trained on Rob2HanD-generated datasets demonstrate strong performance across a variety of customized interaction tasks.

The conflict between the rapid iteration demand of prototyping and the time-consuming nature of user tests has led researchers to adopt AI methods to identify usability issues. However, these AI-driven methods concentrate on evaluating the feasibility of a system, while often overlooking the influence of specified user characteristics and usage contexts. Our work proposes a tool named SimUser based on large language models (LLMs) with the Chain-of-Thought structure and user modeling method. It generates usability feedback by simulating the interaction between users and applications, which is influenced by user characteristics and contextual factors. The empirical study (48 human users and 21 designers) validated that in the context of a simple smartwatch interface, SimUser could generate heuristic usability feedback with the similarity varying from 35.7% to 100% according to the user groups and usability category. Our work provides insights into simulating users by LLM to improve future design activities.

Radiometric compensation has made it possible for a projector to display on ordinary surface with colors and textures. For previous methods, itpsilas necessary to calibrate both the projector and camera at first. The calibration can be time-consuming and needs to be redone once the system settings change. We present a method that simplifies the calibration process. As a result, the system is more practicable for ad-hoc setups.