Yang Liu

Tanren Liu, Xianjia Meng, Yang Liu et al.

Statistical analysis on sensitive datasets like medical records and financial transactions is essential for decision-making, but raises significant privacy concerns. While existing secure Two-Party Computation (2PC) makes extensive efforts in designing the common secure primitives (e.g., addition and multiplication) or machine learning-related functions, few pay attention to the statistical functions. In this paper, we propose SafeStats, a secure toolkit tailored for 2PC secure statistical analysis. Specifically, to develop SafeStats, we first refer to Microsoft Excel's statistical library and summarize that most statistical operations can be achieved with three core functions:1) frequency counting, 2) sorting, and 3) non-linear math functions. Then, for each core statistical function, SafeStats presents an efficient 2PC implementation. For secure frequency counting, SafeStats adopts a secure shift-based strategy to avoid invoking expensive 2PC equality test protocols. For secure sort, SafeStats involves a secure segment-indicator protocol to achieve secure counting-based sort, which enables fast element sorting over specific statistical scenarios without the need for secure comparison. For non-linear math functions, we enhance the current reduce-then-approximate paradigm by introducing a bisection-based range reduction protocol. Finally, we implement SafeStats and test it on 14 common statistical analysis cases. As an example, for the chi-square test, SafeStats achieves a 1.5 $\times$ runtime speedup and a 4.2 $\times$ reduction in communication compared to directly using the current general-purpose 2PC library to realize it.

Zheng Tong, Yang Liu, Wanshu Fan et al.

Large language models and multimodal foundation models are enabling medical artificial intelligence (AI) systems to move beyond isolated prediction and undertake multistep clinical tasks that require planning, tool use, memory, iterative correction, and coordination among specialized agents. However, the scope of agentic AI in medicine remains unsettled, and current evaluation practices are not yet aligned with the requirements of clinical use. We conducted a scoping review with systematic evidence mapping across five electronic sources, screened 1,649 exportable records, and provisionally included 557 unique studies that met predefined criteria for goal-directed task execution, tool use, interaction with external resources, feedback-based refinement, or multi-agent collaboration. The included studies describe single agents that use external tools, workflows supported by retrieval and external knowledge, multimodal agents, and multi-agent systems applied to medical question answering, image interpretation, electronic health record analysis, drug safety, and clinical trial prediction. The evidence base remains dominated by public benchmarks, simulated settings, retrospective datasets, and small-scale expert evaluation. Process reliability, evidence traceability, uncertainty, safety, workflow impact, and external validity are evaluated less consistently. Clinical translation will depend on clearer definitions, reproducible evaluation, auditable oversight, interoperable system design, and prospective validation in real-world clinical workflows.

Chenrui Shi, Yuwei Wu, Yang Liu et al.

Graphical user interface task evaluation aims to determine whether a GUI agent has successfully completed a user instruction. Automated GUI task evaluation has received increasing attention because the evaluation results can serve as reward signals for both test-time scaling and post-training. However, reliable GUI task evaluation remains challenging because the judgments often require access to environment states, such as system configurations, file data, and application settings, beyond the screenshots of execution trajectories. In this paper, we propose an interactive reward agent (IRA) based on a propose-then-verify framework to acquire and verify evidence from the post-execution environment. Given a task instruction and a GUI environment after the GUI agent execution, IRA first proposes the task completion conditions and then verifies them by invoking system tools, application tools, and GUI tools. This design combines evidence from both visible interfaces and the environment state in an interactive process. We further introduce GUI-RewardBench, a benchmark of 321 GUI task trajectories spanning 10 Ubuntu desktop application categories. Experiments show that IRA achieves 86.9% accuracy on GUI-RewardBench, outperforming existing evaluator baselines. We further apply IRA to reinforcement learning of GUI agents, achieving a 34.0% OSWorld success rate, which demonstrates that IRA can provide effective reward signals for training GUI agents.