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What is MathJax?

MathJax is a highly adaptable JavaScript display engine specifically created for mathematical content, guaranteeing uniform performance across all web browsers. It provides beautiful and accessible mathematical displays without requiring any setup from users—MathJax functions smoothly and intuitively. This robust tool allows for the transformation of traditional print resources into modern, web-optimized formats and ePubs. The committed MathJax team offers comprehensive training sessions for your staff, concentrating on how to utilize our tools for creating online educational resources and developing accessible STEM materials. Moreover, MathJax's adaptability permits custom configurations tailored to your institution's unique requirements, including individualized settings and specialized software workflows. By employing CSS with web fonts or SVG rather than bitmap images or Flash, MathJax guarantees that equations are scalable and align seamlessly with surrounding text, regardless of zoom levels. Its modular architecture accommodates multiple input formats such as MathML, TeX, and ASCIImath, while yielding outputs in HTML+CSS, SVG, or MathML. In addition, MathJax is designed to be compatible with screen readers and enhances the user experience through features like expression zoom and interactive exploration, providing significant benefits for educators and learners alike. With its extensive capabilities, MathJax stands out as an essential tool for advancing mathematical communication in educational settings.

What is DeepScaleR?

DeepScaleR is an advanced language model featuring 1.5 billion parameters, developed from DeepSeek-R1-Distilled-Qwen-1.5B through a unique blend of distributed reinforcement learning and a novel technique that gradually increases its context window from 8,000 to 24,000 tokens throughout training. The model was constructed using around 40,000 carefully curated mathematical problems taken from prestigious competition datasets, such as AIME (1984–2023), AMC (pre-2023), Omni-MATH, and STILL. With an impressive accuracy rate of 43.1% on the AIME 2024 exam, DeepScaleR exhibits a remarkable improvement of approximately 14.3 percentage points over its base version, surpassing even the significantly larger proprietary O1-Preview model. Furthermore, its outstanding performance on various mathematical benchmarks, including MATH-500, AMC 2023, Minerva Math, and OlympiadBench, illustrates that smaller, finely-tuned models enhanced by reinforcement learning can compete with or exceed the performance of larger counterparts in complex reasoning challenges. This breakthrough highlights the promising potential of streamlined modeling techniques in advancing mathematical problem-solving capabilities, encouraging further exploration in the field. Moreover, it opens doors for developing more efficient models that can tackle increasingly challenging problems with great efficacy.

Media

Media

Integrations Supported

MathML Kit

Integrations Supported

MathML Kit

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

MathJax

Date Founded

2009

Company Location

United States

Company Website

www.mathjax.org

Company Facts

Organization Name

Agentica Project

Date Founded

2025

Company Location

United States

Company Website

agentica-project.com

Categories and Features

Categories and Features

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