MAML: Towards a Faster Web in Developing Regions

• URL: http://arxiv.org/abs/2502.15708v1
• Date: Mon, 20 Jan 2025 18:35:53 GMT
• Title: MAML: Towards a Faster Web in Developing Regions
• Authors: Ayush Pandey, Matteo Varvello, Syed Ishtiaque Ahmed, Shurui Zhou, Lakshmi Subramanian, Yasir Zaki,
• Abstract summary: Mobile Application Markup Language (MAML) is a flat layout-based web specification language.<n>MAML is backward compatible as it can be transpiled to minimal HTML/JavaScript/CSS.<n>MAML offers webpage speedups by tens of seconds under challenging network conditions.
• Score: 15.590501918707337
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The web experience in developing regions remains subpar, primarily due to the growing complexity of modern webpages and insufficient optimization by content providers. Users in these regions typically rely on low-end devices and limited bandwidth, which results in a poor user experience as they download and parse webpages bloated with excessive third-party CSS and JavaScript (JS). To address these challenges, we introduce the Mobile Application Markup Language (MAML), a flat layout-based web specification language that reduces computational and data transmission demands, while replacing the excessive bloat from JS with a new scripting language centered on essential (and popular) web functionalities. Last but not least, MAML is backward compatible as it can be transpiled to minimal HTML/JavaScript/CSS and thus work with legacy browsers. We benchmark MAML in terms of page load times and sizes, using a translator which can automatically port any webpage to MAML. When compared to the popular Google AMP, across 100 testing webpages, MAML offers webpage speedups by tens of seconds under challenging network conditions thanks to its significant size reductions. Next, we run a competition involving 25 university students porting 50 of the above webpages to MAML using a web-based editor we developed. This experiment verifies that, with little developer effort, MAML is quite effective in maintaining the visual and functional correctness of the originating webpages.

Related papers

• WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation [37.33126974128603]
  WebMMU is a benchmark that evaluates three core web tasks.<n>It unifies website visual question answering, code editing involving HTML/CSS/JavaScript, and mockup-to-code generation.<n>Our evaluation shows that while multimodal large language models (MLLMs) perform well on basic information extraction, they struggle with reasoning and grounding.
  arXiv  Detail & Related papers  (2025-08-22T19:41:02Z)
• Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs [112.89665642941814]
  Multimodal large language models (MLLMs) have shown impressive success across modalities such as image, video, and audio. Current MLLMs are surprisingly poor at understanding webpage screenshots and generating their corresponding HTML code. We propose a benchmark consisting of a new large-scale webpage-to-code dataset for instruction tuning.
  arXiv  Detail & Related papers  (2024-06-28T17:59:46Z)
• AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation [54.17246674188208]
  Web scraping is a powerful technique that extracts data from websites, enabling automated data collection, enhancing data analysis capabilities, and minimizing manual data entry efforts. Existing methods, wrappers-based methods suffer from limited adaptability and scalability when faced with a new website. We introduce the paradigm of generating web scrapers with large language models (LLMs) and propose AutoScraper, a two-stage framework that can handle diverse and changing web environments more efficiently.
  arXiv  Detail & Related papers  (2024-04-19T09:59:44Z)
• WebCode2M: A Real-World Dataset for Code Generation from Webpage Designs [49.91550773480978]
  This paper introduces WebCode2M, a new dataset comprising 2.56 million instances, each containing a design image along with the corresponding webpage code and layout details. To validate the effectiveness of WebCode2M, we introduce a baseline model based on the Vision Transformer (ViT), named WebCoder, and establish a benchmark for fair comparison. The benchmarking results demonstrate that our dataset significantly improves the ability of MLLMs to generate code from webpage designs.
  arXiv  Detail & Related papers  (2024-04-09T15:05:48Z)
• VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding? [115.60866817774641]
  Multimodal Large Language models (MLLMs) have shown promise in web-related tasks. evaluating their performance in the web domain remains a challenge due to the lack of comprehensive benchmarks. bench is a multimodal benchmark designed to assess the capabilities of MLLMs across a variety of web tasks.
  arXiv  Detail & Related papers  (2024-04-09T02:29:39Z)
• A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis [69.15016747150868]
  We introduce WebAgent, an agent that learns from self-experience to complete tasks on real websites. WebAgent plans ahead by decomposing instructions into canonical sub-instructions, summarizes long HTML documents into task-relevant snippets, and acts on websites. We empirically demonstrate that our modular recipe improves the success on real websites by over 50%, and that HTML-T5 is the best model to solve various HTML understanding tasks.
  arXiv  Detail & Related papers  (2023-07-24T14:56:30Z)
• Mind2Web: Towards a Generalist Agent for the Web [25.363429937913065]
  Mind2Web is the first dataset for developing and evaluating generalist agents for the web. With over 2,000 open-ended tasks collected from 137 websites spanning 31 domains, Mind2Web provides three necessary ingredients for building generalist web agents. Based on Mind2Web, we conduct an initial exploration of using large language models (LLMs) for building generalist web agents.
  arXiv  Detail & Related papers  (2023-06-09T17:44:31Z)
• WebSHAP: Towards Explaining Any Machine Learning Models Anywhere [13.883867498610172]
  We present WebSHAP, the first in-browser tool that adapts the state-of-the-art model-agnostic explainability technique SHAP to the Web environment. Our open-source tool is developed with modern Web technologies such as WebGL that leverage client-side hardware capabilities.
  arXiv  Detail & Related papers  (2023-03-16T17:56:02Z)
• Understanding HTML with Large Language Models [73.92747433749271]
  Large language models (LLMs) have shown exceptional performance on a variety of natural language tasks. We contribute HTML understanding models (fine-tuned LLMs) and an in-depth analysis of their capabilities under three tasks. We show that LLMs pretrained on standard natural language corpora transfer remarkably well to HTML understanding tasks.
  arXiv  Detail & Related papers  (2022-10-08T07:27:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.