JBKing514/map_blog
The Manifold Alignment Protocol (MAP) is a geometric analysis framework for studying how complex systems—such as large language models, diffusion models, or physical measurement pipelines—converge, stabilize, and align under iterative processes.
The Manifold Alignment Protocol (MAP) helps scientists and engineers understand how complex AI models and physical systems behave and change over time. It takes high-dimensional data from systems like large language models or radio signals and provides visual dashboards showing how they converge, stabilize, or change. This allows researchers, AI safety engineers, and signal processing experts to interpret complex system dynamics through clear geometric patterns.
Use this if you need a standardized way to analyze and visualize the stability, convergence, and safety of complex 'black box' systems, whether they are AI models or physical measurement pipelines.
Not ideal if you are looking for a tool to directly manipulate or bypass AI safety guardrails, as its purpose is observability and steerability.
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Language
JavaScript
License
MIT
Category
Last pushed
Jan 01, 2026
Commits (30d)
0
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