arXiv subjects

WenHao Wang

Publications and source records attributed to WenHao Wang.

6 recordsLinked to original sources

arXiv2026

MemGUI-Bench: Benchmarking Memory of Mobile GUI Agents in Dynamic Environments

Reliable mobile GUI agents must retain and reuse information across actions, applications, and repeated interactions. However, current benchmarks systematically underrepresent these memory demands: only 5.2-11.8 percent of their tasks are memory-related, and none evaluates cross-session learning. We introduce MemGUI-Bench, a comprehensive memory-centric benchmark that assesses both short-term information retention and long-term experience accumulation through pass@k protocols and staged LLM-as-judge evaluation. Our contributions include: (1) a systematic taxonomy of short- and long-term memory based on 11 agents across 5 architectures; (2) a snapshot-based suite of 128 tasks across 26 applications, organized into 64 mirror pairs, where 89.8 percent require cross-temporal and cross-spatial retention; (3) MemGUI-Eval, an automated 3-stage Progressive Scrutiny pipeline with 7 hierarchical metrics spanning memory fidelity, learning effectiveness, and execution efficiency; and (4) an assessment of 11 state-of-the-art agents guided by 6 research questions. Our experiments reveal substantial memory deficits across all evaluated systems, including 4-10x capability gaps on memory-intensive tasks. They further show that short-term memory is indispensable, while explicit long-term memory improves cross-session learning by 21.9 percentage points, with cross-application transfer and computational cost remaining major bottlenecks. We additionally identify 5 distinct failure modes and synthesize 5 actionable design implications for future memory-enhanced agents. All resources, including code, benchmark, and evaluation results, will be fully open-sourced and continuously maintained at https://memgui-bench.github.io/.

cs.DC↗

arXiv2026

Towards Recursive Self-Evolving Agentic Literature Retrieval

Scientific literature retrieval must understand complex search intents while preserving source authenticity. Traditional keyword and embedding-based systems return authentic sources but miss nuanced intents, whereas large language models capture richer intents but may fabricate citations. We introduce PaSaMaster, a Recursive Self-Evolving agentic literature retrieval system that iteratively analyzes intent, retrieves verified papers and ranks them with evidence-grounded relevance scores. PaSaMaster combines self-evolving retrieval that refines search intent from ranked evidence over time, hallucination-free ranking over verified papers rather than generated citations, and cost-efficient planning--retrieval separation that reserves frontier LLMs for intent understanding while delegating retrieval and scoring to lightweight models and customized corpora. Across 38 disciplines in PaSaMaster-Bench, PaSaMaster achieves a 16.5$\times$ higher F1-score than Google Scholar and a 37.8\% higher F1-score than GPT-5.2 at about 1\% of the cost, while reducing source hallucination from 32.66\% in generative LLMs to zero: https://github.com/sjtu-sai-agents/PaSaMaster

cs.IR↗

arXiv2026

DataMaster: Data-Centric Autonomous AI Research

As model families, training recipes, and compute budgets become increasingly standardized, further gains in machine learning systems depend increasingly on data. Yet data engineering remains largely manual and ad hoc: practitioners repeatedly search for external datasets, adapt them to existing pipelines, validate candidate data through downstream training, and carry forward lessons from prior attempts. We study task-conditioned autonomous data engineering, where an autonomous agent improves a fixed learning algorithm by optimizing only the data side, including external data discovery, data selection and composition, cleaning and transformation. The goal is to obtain a stronger downstream solution while leaving the learning algorithm unchanged. To address the open-ended search space, branch-dependent refinement, and delayed validation inherent in autonomous data engineering, we propose DataMaster, a data-agent framework that integrates tree-structured search, shared candidate data, and cumulative memory. DataMaster consists of three key components: a DataTree that organizes alternative data-engineering branches, a shared Data Pool that stores discovered external data sources for reuse, and a Global Memory that records node outcomes, artifacts, and reusable findings. Together, these components allow the agent to discover candidate data, construct executable training inputs, evaluate them through downstream feedback, and carry useful evidence across branches. We evaluate DataMaster on two types of benchmarks, MLE-Bench Lite and PostTrainBench. On MLE-Bench Lite, it improves medal rate by 32.27% over the initial score; on PostTrainBench, it surpasses the instruct model on GPQA (31.02% vs 30.35%).

cs.LG↗

arXiv2026

MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents

Shortcuts such as APIs and deep-links have emerged as efficient complements to flexible GUI operations, fostering a promising hybrid paradigm for MLLM-based mobile automation. However, systematic evaluation of GUI-shortcut hybrid agents remains largely underexplored. To bridge this gap, we introduce MAS-Bench, a benchmark that pioneers the evaluation of GUI-shortcut hybrid agents with a specific focus on the mobile domain. Beyond merely using predefined shortcuts, MAS-Bench assesses an agent's capability to autonomously generate shortcuts by discovering and creating reusable, low-cost workflows. It features 139 complex tasks across 11 real-world applications, a knowledge base of 88 predefined shortcuts (APIs, deep-links, RPA scripts), and 9 evaluation metrics. Experiments demonstrate that hybrid agents achieve up to 68.3% success rate and 39% greater execution efficiency than GUI-only counterparts. Furthermore, our evaluation framework effectively reveals the quality gap between predefined and agent-generated shortcuts, validating its capability to assess shortcut generation methods. MAS-Bench addresses the lack of systematic benchmarks for GUI-shortcut hybrid mobile agents, providing a foundational platform for future advancements in creating more efficient and robust intelligent agents. Project page: https://pengxiang-zhao.github.io/MAS-Bench.

cs.AI↗

arXiv2025

LLM-Powered GUI Agents in Phone Automation: Surveying Progress and Prospects

With the rapid rise of large language models (LLMs), phone automation has undergone transformative changes. This paper systematically reviews LLM-driven phone GUI agents, highlighting their evolution from script-based automation to intelligent, adaptive systems. We first contextualize key challenges, (i) limited generality, (ii) high maintenance overhead, and (iii) weak intent comprehension, and show how LLMs address these issues through advanced language understanding, multimodal perception, and robust decision-making. We then propose a taxonomy covering fundamental agent frameworks (single-agent, multi-agent, plan-then-act), modeling approaches (prompt engineering, training-based), and essential datasets and benchmarks. Furthermore, we detail task-specific architectures, supervised fine-tuning, and reinforcement learning strategies that bridge user intent and GUI operations. Finally, we discuss open challenges such as dataset diversity, on-device deployment efficiency, user-centric adaptation, and security concerns, offering forward-looking insights into this rapidly evolving field. By providing a structured overview and identifying pressing research gaps, this paper serves as a definitive reference for researchers and practitioners seeking to harness LLMs in designing scalable, user-friendly phone GUI agents. The collection of papers reviewed in this survey will be hosted and regularly updated on the GitHub repository: https://github.com/PhoneLLM/Awesome-LLM-Powered-Phone-GUI-Agents

cs.HC↗

arXiv2025

VLMGuard-R1: Proactive Safety Alignment for VLMs via Reasoning-Driven Prompt Optimization

Aligning Vision-Language Models (VLMs) with safety standards is essential to mitigate risks arising from their multimodal complexity, where integrating vision and language unveils subtle threats beyond the reach of conventional safeguards. Inspired by the insight that reasoning across modalities is key to preempting intricate vulnerabilities, we propose a novel direction for VLM safety: multimodal reasoning-driven prompt rewriting. To this end, we introduce VLMGuard-R1, a proactive framework that refines user inputs through a reasoning-guided rewriter, dynamically interpreting text-image interactions to deliver refined prompts that bolster safety across diverse VLM architectures without altering their core parameters. To achieve this, we devise a three-stage reasoning pipeline to synthesize a dataset that trains the rewriter to infer subtle threats, enabling tailored, actionable responses over generic refusals. Extensive experiments across three benchmarks with five VLMs reveal that VLMGuard-R1 outperforms four baselines. In particular, VLMGuard-R1 achieves a remarkable 43.59\% increase in average safety across five models on the SIUO benchmark.

cs.LG↗