CCSLM and Collision Decision Trees

 CCSLM and Collision Decision Trees



What happens when a neural network reuses a finite set of linear mappings across many different decision paths?

This note explores Concatenated Context Selected Linear Mappings (CCSLM), where context selects the computation and linear mappings transform the data. Reusing mappings creates collision decision trees: distinct paths can share the same computational operator, while linear associative memory allows that operator to respond differently to different inputs.

The report concludes with three fundamental computational elements: decision-tree collisions, geometric reorientations, and singular-value modulation. Together, they offer a compact framework for thinking about context-dependent computation, parameter reuse, and the geometry of neural networks.

Note: Collision Decision Trees

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