From clicking on the consultation: Webcharkature Benchmark challenge agents agents in memorial and most pages

Web Automation Agents have turned into the growing focus on artificial intelligence, especially because of their ability to produce acts such as the digital areas. These agents are in contact with the Graphical user interface (Guis), imitate the behavior of a person such as clicking, typing, and navigation in Web pages. This method goes through the need for programming applications for program apps, which are usually not available or restricted to many web applications. Instead, these agents can apply to all international backgrounds, allowing them to exercise a variety of comprehensive job tools. The emergence of large languages ​​(LLMS) enables these agents not only for the content of web and again, the system, and do increasing acts. As their skills grow, so does the need to evaluate more than simple browsing work. Bentmarkers let you be enough for the first models that are no longer able to fully measure the skills of today agents.

As the web workers make progress, oppressive debate arises: their skills management skills with detailed activities, and many measures remain unequal. Many jobs are people doing to websites, such as retrieving data from different pages, to make the calculation based on previous installation, or to apply complex laws, requires an important understanding effort. These are not just the challenges of transport; They test memory, reasonable, and for a long time. However, many benches focus on simplified situations, which fails to show the types of digital functions that people often avoid. In addition, the limitations of these benches have seemed to be agents that improve their performance. Aliguities in task instructions or non-compliance in expected results begins for Skew test. When agents produce a slow but slow response, they are punished wrongly because of the meanings of work. Such mistakes make it difficult to distinguish between the estimated model and benchchmark.

Previous attempts to test the web-centered web-centered web supporters such as the Webarena. Webrarena received a very comprehensive care due to its reproduction and ability to imitate the world's true websites, including Reddits, Gitlab, and commerce platforms. It can provide more than 800 tasks designed to exercise the agent that is based on the web within these areas. However, these functions are focused on normal criticism and has not been very infected. Other benches, such as Mind2web, GAIA, and Mmin, have an impact on examining the web functions or special platform areas such as the services, but each one came with traders-offs. Some who did not work together, some supporters of fertility, while others were very released. This estimated created a gap in estimating agent's progress in areas that require complex decisions, long-term memory, and accurate data operations in all many Web pages.

Investigators from the University of Tokyo are silent of Webcharkyena. This exposter is expanded by the Webarena structure but greater functions of jobs and difficulties. The Webcorebube includes a total of 532 jobs recently decided, distributed to all the four identical websites used. These tasks are designed to be very desired, to show conditions where agents should be involved in activities such as data integration, remembrance, and several different consultation. Importantly, the bench is constructed to ensure full and common recycling, enabling good comparisons between agents and avoiding ambiguists from previous tools. The installation of different activities and resources for installing helps to imitate logical broadcasting and testing agents on a working scale and challenging scale.

Webcorenena separates its functions with four main types. One hundred and seventeen works fall under a great memory, requiring agents to extract and remember large numbers of information, such as combining all the customers linked to high transaction. Counting activities, including 132 entries, includes arithmetic functions such as high-monthly spending months based on multiple data points. Long-long memory activities and test the agent's ability to connect information to all different pages, such as retirement rates from one site and use it to another. Additional 65 tasks are divided into 'others', including activities such as providing customized Gitlab labels of traditional formats. Each employee specifies its installation system, with 451 tasks resolved in any form of view, 69 requiring only a text input, and depends on only 12 input.

In the bench test, researchers use three large models of large language: GPT-4O, Claude 3.7 Sonnet, and Gemini 2.5 Pro. This was tested in conjunction with two advanced web agents, AgeCambam and Brersonchym. The results highlighted the growing difficulties of Bcerceaeran compared to previous benches. GPT-4O, had received 42.8% accuracy in the Suxuration, is only controlled by 6.8% in the week. Claude 3.7 Sonnet and Gemini 2.5 Pro is better made, with a germin that reaches the highest 44.9% accuracy. Unless you are a higher actor, the result has shown important spaces in working when facing the complexity of the B'choreanena. The bench also reflects the most sensitive to finding a contrast between models, which makes it an important tool for measuring ongoing development in the web agent technology.

A few important ways from research includes:

• The Webcorealairena includes 532 functions: Greater memory is 117, 132 counts, longevity, 127 memory, and 65 others.
• Jobs are distributed across stores (117), shop management (132), Reddit (91), gitlab (127), and 65 crash conditions.
• Input types: 451 functions are resolved for any installation, 69 requires text input, and 12 require the implementation.
• GPT-4O has only ruled 6.8% in you BCoreral compared to 42.8% in the Webarena.
• Gemini 2.5 Pro Pro wins the highest points in 44.9%, showing current limitations in managing complex tasks.
• Webcorerena provides clear function between models than the Webarena, to improve the number of symptom.
• The total number of 117 job templates are used to ensure diversity and recycling areas of 4,5 per template.
• The bench looks for more than 300 hours of adjective and analysis, which indicates its strong construction.
• To check using the matches of string, comparing the URL, and the comparisons of the HTML structure testing accuracy.

In conclusion, this study highlights differences between the general browsing technology and comprehension skills required for the Web-based activities. The new WebcoreArage is introduced as a solid and detailed benched bench designed to pressure web agents in areas where they should depend on the consultation, memory, and mind. It replaces the Ambiguity with Standation, and its functions imitate the Digital Digidisher agents that should learn how to handle real jobs.

Look at the paper, GitHub and Project Page. All credit for this study goes to research for this project.

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