A Coding Tutorial on OpenMythos on Recurrent-Depth Transformers with Depth Extrapolation, Adaptive Computation, and Mixture-of-Experts Routing

MarkTechPost / 4/24/2026

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Key Points

The tutorial walks through implementing OpenMythos, a theoretical reconstruction inspired by the Claude Mythos idea, aiming to enable deeper reasoning via iterative computation rather than scaling up parameter counts.
It covers building and analyzing models that use GQA and MLA attention mechanisms, comparing their behavior and implementation details.
The post evaluates memory efficiency by comparing KV-cache usage across setups, highlighting how iterative/depth-related computation impacts resource demands.
It discusses stability validation using spectral properties of intermediate computations, suggesting how to check whether the recurrent-depth approach remains well-behaved.
The tutorial integrates advanced components such as depth extrapolation, adaptive computation, and Mixture-of-Experts routing into the overall system design.

In this tutorial, we explore the implementation of OpenMythos, a theoretical reconstruction of the Claude Mythos architecture that enables deeper reasoning through iterative computation rather than increased parameter size. We build and analyze models using both GQA and MLA attention mechanisms, examine memory efficiency through KV-cache comparisons, and validate stability via the spectral properties of […]

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