# UI-Venus-1.5 Technical Report

Source: https://www.youtube.com/watch?v=cwOmj_JJbZY
Recap page: https://rapidrecap.app/video/cwOmj_JJbZY
Generated: 2026-02-14T01:34:58.751+00:00

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## Quick Overview

The UI-Venus-1.5 agent successfully bridges the gap between human intent and computer execution by using a visual-only approach, achieving a 76% success rate on the Android benchmark while avoiding the complexity and potential errors associated with parsing underlying code or XML structures.

**Key Points:**
- UI-Venus-1.5 achieved a 76% success rate on the Android benchmark, significantly outperforming previous models like MAI-UI and MAI-UI 3.
- The agent is designed to operate visually, interpreting screen elements like pixels and button locations just as a human would, rather than parsing underlying code or XML.
- This visual-only approach bypasses the common issue in older methods where models were heavily rewarded for following procedural steps rather than achieving the actual user goal (e.g., booking a flight).
- The report highlights that the training heavily focused on real-world optimization, specifically teaching the model to refuse unsafe or unethical instructions and to understand the consequences of actions.
- The model's architecture is a unified 8B parameter model, merging the strengths of specialized models (like those for vision or code) into a single, more robust agent.
- A key limitation noted is that the agent must play out the entire task sequence to receive a reward signal, which can introduce latency and computational expense.

![Screenshot at 00:09: The agent is fundamentally designed as an end-to-end UI agent that actively controls a graphical user interface, contrasting with models that rely on reading code or XML structures.](https://ss.rapidrecap.app/screens/cwOmj_JJbZY/00-00-09.jpg)

**Context:** This technical report discusses the performance and architecture of the UI-Venus-1.5 agent, developed by the Venus team at Ant Group, which specializes in end-to-end agents capable of interacting with graphical user interfaces. The discussion contrasts this new approach with older models that relied on analyzing underlying code structures (like XML) or explicit step-by-step instructions, emphasizing the importance of visual understanding and real-world context for reliable agent deployment.

## Detailed Analysis

The UI-Venus-1.5 agent represents a significant pivot in end-to-end AI agents, focusing on visual interaction rather than parsing underlying code or structured data like XML. Developed by the Venus team at Ant Group, this agent achieved a 76% success rate on the Android benchmark, outperforming previous models like MAI-UI 3 and MAI-UI by a significant margin. The core innovation is its ability to 'see' the screen, interpret pixel coordinates for interactive elements (like buttons or menus), and understand the consequences of actions based on visual feedback, essentially mimicking human visual processing. This procedural knowledge (how to interact) is explicitly trained, allowing the agent to execute complex multi-step tasks like booking flights or shopping online without needing explicit code instructions. The training emphasizes safety by heavily penalizing actions that lead to unsafe, unethical, or impossible outcomes, ensuring real-world viability. A notable trade-off is that the agent must execute the entire task sequence to receive a final reward signal, which can lead to latency and high computational cost. However, the resulting unified 8B parameter model is suggested to be more robust than previous specialized models by merging vision and language skills into a single architecture, offering a simpler path for deploying agents on local hardware.

### Agent Overview and Performance

- UI-Venus-1.5 is an end-to-end UI agent achieving 76% success rate on Android benchmark
- Outperforms previous models like MAI-UI 3 and MAI-UI
- Uses visual input (pixels) instead of underlying code/XML for interaction

### Training Methodology

- Training heavily focused on real-world optimization and safety
- Model learns to refuse unsafe/unethical instructions
- Uses Online Reinforcement Learning with full trajectory rollout before rewarding success

### Key Architectural Differences

- Merges specialized skills (vision, language, control) into a unified 8B parameter model
- Avoids reliance on brittle XML parsing or explicit step-by-step instructions common in older methods

### Limitations and Future Implications

- Agent must complete the entire task sequence for reward, causing latency
- The visual approach suggests a future where agents operate more like humans on local devices, decoupled from the OS.

![Screenshot at 00:00: Initial screen showing the podcast branding and the call to 'Become A Member Today!' within a circular grid overlay.](https://ss.rapidrecap.app/screens/cwOmj_JJbZY/00-00-00.jpg)
![Screenshot at 00:09: The speaker explicitly defines the agent as an end-to-end UI agent whose purpose is to actively control a graphical user interface.](https://ss.rapidrecap.app/screens/cwOmj_JJbZY/00-00-09.jpg)
![Screenshot at 00:54: Example task flow failure: The agent is shown trying to click a button that is visually covered by a pop-up ad, demonstrating a failure in context awareness.](https://ss.rapidrecap.app/screens/cwOmj_JJbZY/00-00-54.jpg)
![Screenshot at 02:23: Visual comparison of task execution: The report implies the 8B model successfully handles complex sequences that older models struggled with.](https://ss.rapidrecap.app/screens/cwOmj_JJbZY/00-02-23.jpg)
![Screenshot at 06:59: The speaker summarizes the success of the unified agent, which fluidly moves between web tasks and mobile tasks without friction.](https://ss.rapidrecap.app/screens/cwOmj_JJbZY/00-06-59.jpg)