BUTTETTEETTENCE GIVE UI-Tars-1.5, The revised version of its multimodal agent focused on the GUI) and the conditions of the game. Designed as the Vision-Figling model is able to recognize the content of the screen and perform links, UI-Tars-1.5 provides consistent development across the Gui Automation list and a consultation benot. Significantly, it exceeds several lead models – including Opelai's Operator and Anthropic's Claude 3.7 – to all accuracy and termination of functions in many areas.
The release continues the phytance monitoring of traditional agents, which aims to unite the understanding, understanding and performing integrated workouts that support direct participation with the GUI and visual content.
The traditional line of the GUI
Unlike the LLMS tool or UI-Call Architectures, UI-Call Architectures, UI-Tars-1.5 training Finally to identify human control actions, such as the Mouse Movement and keyboard installation. This position is a model nearby how people users communicate with digital systems.
The UI-Tars-1.5 builds from its account for the introduction of multiple buildings and training:
- Understanding and integration of consultation: Combined model that combines the screen pictures and text instructions, supporting the complex understanding of the work and the basis of viewing. Reasoning is supported by a multi-step “step – and then acts, separating the higher planning from low murder.
- A space of a combined action: The act of action is designed to be a platform for the Agnostic, enables a fixed interface across the desktop site, mobile, and the game.
- Evolution by following traces: Training pipe includes visual data. This allows the model to postpone its behavior by analyzing previous communication – reducing reliance on selected display.
This development empowering UI-Tars-1.5 to support the leading partnership, recovery, and planning for planning – the essential power of the UI and control.
Estimate and evaluation
The model is tested for several Benchmark suites evaluates the agency's behavior to both GUI activities and activities based on the game. These bencmmarks provide a common method of evaluating the functioning of the model across the consultation, placement, and long execution.
GUI agent's activities
- Osworld (100 Steps): UI-Tars-1.5 Reaches 42.5% successful amount, OutperforghormFormForm Operai opera (36.4%) and Claude 3.7 (28%). Benchmark is evaluated the teenage tasks of a long gui in the creation of OS.
- Windows agent Arena (50 steps): Making 42.1%, the model is very upgrading the previous bases (eg 29.8%), indicates the power management of desktop positions.
- Android world: The model reaches 64.2% successful average, suggesting garymabilities in mobile apps.
The sophisticated basis and understanding of the screen
- ScreensPot-V2: The model reaches 94.2% accuracy in finding gui items, EfterformlformBorf operators (87.9%) and Claude 3.7 (87.6%).
- ScreensPotPro: The basic basic, Ui-Tars-1.5 scores 61.6%, before operator (23.4%) and Claude 3.7 (27.7%).
These effects indicate consistent improvements in the screening of the screen and the action of action, which is the most important of the Real-World Gui Agents.
Places of the game
- Poki Games: UI-Tars-1.5 Reaches 100% termination rate in 14 mini games – games. These sports vary from commercial and context, which require models to use dynamic energy.
- MINECRAFT (MINL): The model reaches 42% of the 32% of the 31% of MOB murder activities when using module “.
Availability and acquisition
UI-Tars-1.5 is open under Apache 2.0 and available for multiple Shipping Options:
In addition to the model, the project provides detailed documentation, reimbursing Data, as well as the assessment tools to facilitate testing and recycling.
Store
UI-Tars-1.5 is a clear technological advancement in the Multimodal Agents, especially those who focus on the GUI control and the visible manifestation. With the integration of the vision vision, memory methods, and formal action, model shows strong performance in a variety of active sites.
Instead of pursuing universal production, the model is organized by multimodal-directed multimodal guidance – targeted by the original world challenge to communicate with the visual understanding. Its open source removal provides an effective framework for investigators and enhancements that are interested in evaluating the agent or automated systems applicable language and vision.
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Nikhil is a student of students in MarktechPost. Pursuing integrated graduates combined in the Indian Institute of Technology, Kharagpur. Nikhl is a UI / ML enthusiasm that searches for applications such as biomoutomostoments and biomedical science. After a solid in the Material Science, he examines new development and developing opportunities to contribute.
