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cs.AI2026

InquireMobile: Teaching VLM-based Mobile Agent to Request Human Assistance via Reinforcement Fine-Tuning

Qihang Ai, Pi Bu, Yue Cao +8

Recent advances in Vision-Language Models (VLMs) have enabled mobile agents to perceive and interact with real-world mobile environments based on human instructions. However, the c…

cs.AI2026

Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training

Jihao Gu, Qihang Ai, Yingyao Wang +10

Vision-language model-based mobile agents have gained the ability to understand complex instructions and mobile screenshots, benefiting from reinforcement learning paradigms like G…

cs.AI2026

How Foundational Skills Influence VLM-based Embodied Agents:A Native Perspective

Bo Peng, Pi Bu, Keyu Pan +7

Recent advances in vision-language models (VLMs) have shown promise for human-level embodied intelligence. However, existing benchmarks for VLM-driven embodied agents often rely on…

cs.AI2025

DeepPHY: Benchmarking Agentic VLMs on Physical Reasoning

Xinrun Xu, Pi Bu, Ye Wang +7

Although Vision Language Models (VLMs) exhibit strong perceptual abilities and impressive visual reasoning, they struggle with attention to detail and precise action planning in co…

cs.AI2025

LongDocURL: a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating

Chao Deng, Jiale Yuan, Pi Bu +8

Large vision language models (LVLMs) have improved the document understanding capabilities remarkably, enabling the handling of complex document elements, longer contexts, and a wi…