# UI-Venus-1.5 Technical Report

Source: https://www.youtube.com/watch?v=AYAk3Fnx9Hk
Recap page: https://rapidrecap.app/video/AYAk3Fnx9Hk
Generated: 2026-02-27T19:04:37.21+00:00

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## Quick Overview

The UI-Venus-1.5 multimodal large language model represents a fundamental shift from text-based processing to a unified end-to-end system capable of perceiving and interacting with visual information like a human user, achieving a 77.6% success rate on the Android world task, significantly outperforming the previous 72B parameter model, which had a 34% success rate on the same task due to its reliance on a fragmented, non-unified architecture.

**Key Points:**
- UI-Venus-1.5 is a new multimodal large language model developed by the Ant Group news team, designed to navigate the digital world by perceiving and interacting with screens visually, much like a human user.
- It achieved a 77.6% success rate on the Android world task, a massive efficiency jump compared to the previous 72B parameter model's 34% success rate on the same task.
- The model uses a unified end-to-end architecture, contrasting with older systems that relied on fragmented, modular approaches requiring separate code bridges for different functions.
- A key feature is its ability to skip cumbersome visual navigation steps (like clicking menus) by using hotkeys or directly acting on visual elements, leading to faster task completion.
- The framework uses a multi-stage training pipeline, starting with mid-training (Knowledge Injection) and moving to offline reinforcement learning (Offline RL) to ensure high precision across all steps.
- The explicit design choice to reject the legacy XML-based approach, which often failed when visual elements changed, validates the need for unified, perception-based agents.
- The success of UI-Venus-1.5 suggests a maturation of AI technology, moving from simple task completion to complex, real-world workflow execution with high reliability.

![Screenshot at 00:14: The announcement of the new 8 billion parameter multimodal large language model, UI-Venus-1.5, developed by Ant Group, signifying a shift toward visual interaction capabilities.](https://ss.rapidrecap.app/screens/AYAk3Fnx9Hk/00-00-14.jpg)

**Context:** This technical report analyzes UI-Venus-1.5, a new multimodal large language model developed by the Ant Group news team. The model marks a significant architectural shift in AI, moving away from relying on explicit code parsing (like XML) to directly perceiving and interacting with graphical user interfaces (GUIs) on screens, similar to how a human operates a device.

## Detailed Analysis

The technical report details the UI-Venus-1.5 model, which represents a significant shift towards an end-to-end multimodal agent capable of visual perception and interaction, contrasting sharply with previous text-based or modular systems. Developed by the Ant Group, the model achieved a notable 77.6% success rate on the Android world task, a substantial improvement over the 72B parameter model's 34% success rate on the same benchmark. The core innovation is its unified architecture, allowing it to process screen data directly, much like a human user. This contrasts with legacy agents that required explicit code parsing (like XML) or reliance on separate, modular components for different tasks (like web navigation vs. mobile navigation). The model's training involves a multi-stage pipeline, including a foundational mid-training stage (Knowledge Injection) followed by Online Reinforcement Learning (Online RL) to handle dynamic, real-world interactions. A key advantage highlighted is the model's ability to execute complex tasks, such as booking a flight or ordering food, by directly interacting with visual elements (clicking buttons, swiping) without needing to parse underlying code or navigate complex menu hierarchies, which significantly reduces latency and increases efficiency. The report also mentions that the model was specifically trained to avoid the pitfalls of older systems, where a small change in the UI blueprint could invalidate the agent. Furthermore, the model demonstrated a high degree of consistency, maintaining high accuracy (90%) across both web and mobile environments, unlike previous models that often required separate, specialized fine-tuning for each platform. The implicit lesson drawn is that explicit visual understanding and unified processing lead to more robust and reliable AI agents capable of handling complex, real-world workflows.

### UI-Venus-1.5 Overview

- Fundamental shift from text-based to visual/multimodal interaction
- Developed by Ant Group
- 8 Billion parameters

### Performance Metrics

- 77.6% success rate on Android world task
- 34% success rate for previous 72B model on same task
- 90% accuracy on web/mobile tasks

### Architectural Shift

- Unified end-to-end system
- Rejects fragmented, modular approaches reliant on code parsing (XML) or separate APIs

### Training Pipeline

- Multi-stage process including Knowledge Injection (Stage 1) and Online Reinforcement Learning (Stage 2)
- Uses explicit visual data (pixel density) for grounding

### Key Advantages

- Skips cumbersome visual navigation (e.g., menus) by using hotkeys or direct visual interaction
- Faster task completion and lower latency

### Critique of Legacy Systems

- Older agents failed if UI blueprints changed
- Reliance on static data sets limited context and led to compounding errors

### Implications

- Demonstrates high reliability and versatility, capable of handling complex workflows across different platforms without explicit task-specific code.

![Screenshot at 00:00: Promotional image for the podcast/channel, featuring two hosts and the text "Become a Member Today!" overlaid on a signal graph.](https://ss.rapidrecap.app/screens/AYAk3Fnx9Hk/00-00-00.jpg)
![Screenshot at 00:14: The introduction slide explicitly names the new model: UI-Venus-1.5, a new multimodal large language model.](https://ss.rapidrecap.app/screens/AYAk3Fnx9Hk/00-00-14.jpg)
![Screenshot at 01:07: A graphic illustrating the core concept: the model interacts with screens by looking at pixel data, much like a human user.](https://ss.rapidrecap.app/screens/AYAk3Fnx9Hk/00-01-07.jpg)
![Screenshot at 02:24: Specific model size mentioned: 30 billion parameter model utilizing a mixture of experts architecture.](https://ss.rapidrecap.app/screens/AYAk3Fnx9Hk/00-02-24.jpg)
![Screenshot at 09:58: A comparison point is made: the failure of models that simply guess target locations versus the reasoned approach of UI-Venus-1.5.](https://ss.rapidrecap.app/screens/AYAk3Fnx9Hk/00-09-58.jpg)