Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents

Quick Overview

The Mobile-Agent-v3.5 model successfully executes complex multi-step tasks across Android, Windows, and Web environments, significantly outperforming the older Cloud-3 Sonnet model by achieving 72.9% success versus 56.5% on the complex memory task involving web navigation and data fetching, demonstrating superior reasoning and cross-platform capability.

Key Points: Mobile-Agent-v3.5 achieved a 72.9% success rate on the complex multi-step memory task, significantly beating the Cloud-3 Sonnet model's 56.5% success rate. The new model excels at inherently visual tasks, such as navigating user interfaces (clicking buttons, scrolling, using menus) across Android, Windows, and Web environments. The architecture uses a multi-agent collaboration framework with distinct roles (Manager, Worker, Reflector, Notetaker) to handle complex tasks without human supervision. The key innovation is the use of world modeling, which allows the agent to predict future screen states and reason about cause and effect, avoiding failure loops. The agent successfully executed a complex task involving finding a hotel under $200 in Tokyo, requiring multiple steps across different applications (Search, Filter by Price, Select Hotel, Email Details). The paper explicitly notes that the new model's architecture is vastly superior to the older Qwen 3VL model, which scored only 18.5% on the same complex memory task.

Context: This video discusses the capabilities and performance benchmarks of the new AI agent model, Mobile-Agent-v3.5, developed by Alibaba Group's Tongyi Lab. The focus is on how this agent handles complex, multi-step interactions across various digital platforms (Android, Windows, Web) by relying on visual reasoning and an internal multi-agent framework, contrasting its performance sharply against previous models like Cloud-3 Sonnet and Qwen 3VL.

Detailed Analysis

The discussion centers on the Mobile-Agent-v3.5 model, emphasizing its ability to execute complex, multi-step tasks across diverse operating systems and interfaces (Android, Windows, Web) without direct human intervention. The core innovation is its use of world modeling, enabling the agent to anticipate future screen states and reason about action consequences, which prevents it from getting stuck in failure loops common to older models that relied only on explicit instructions or visual feedback like button location. The agent architecture employs four distinct roles: Manager (planning), Worker (execution), Reflector (quality control), and Notetaker (memory). In a critical test, the agent achieved a 72.9% success rate on a complex memory task involving booking a hotel, significantly outperforming the 56.5% success rate of the Cloud-3 Sonnet model and the 18.5% rate of the Qwen 3VL model on the same task. The performance improvement is attributed to the agent's ability to reason about the visual flow of an application, such as knowing where the 'File' menu is before the user clicks an icon. The paper also notes that the model is optimized for executing these actions locally on a device, which preserves privacy by not sending user data to the cloud for processing.