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Soo been obsessing over matrix & theory

Thinking options for most efficient application using concept of a merkle tree for a matrix; still requiring efficient merkle verification (tree=type of graph)

Saw merkle field= each layer, merging into node

merkle tree isnt only classico binary

But low-res; so horizontal+vertical row hashing=faster & then found A sci-article; just 1 on subject & 0 libs

Goal= (+) merkle matrix library

Expanding on the Merkle Field, instead of just dealing merging horizontal rows, we merge vertical rows and then you have two routes to verification, and still few verification but still not very resolute. In order to verify a single block without the row you are required to do both verification steps. Or wait untill you have a horizontal row or vertical row and simply do one verification step.
The real magic is when you take the binary Merkle Trees to represent vertical and horizontal rows, and then merging those two trees into the root node for a single merkle matrix. Now what I think would be interesting is both using 4, resulting in 4 roots, and you only need a single 1 to validate any block quickly within the matrix. And then different members of say a network could have different trees to offer for validation. Most importantly, I want to take this and add an extra dimensions (probably more than 1), and I know about verkle trees but I don't want a simple tree, I want a sparse matrix ideally, or a 3D matrix, using at least 3 trees, combining into a single root node. This could be used for verifiable time series data. What I plan to do is store essentially a mutable torrent inside each block in the matrix. And changes are represented over time as each "stack" is added to the matrix to make it 3D (or more)

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