  This paper introduces an innovative method for traversing binary decision
trees using arithmetic operations. We present a suite of binary tree traversal
algorithms that leverage novel representation matrices to flatten the full
binary tree structure and embed the aggregated internal node decisions into a
single vector. Our approach, grounded in maximum inner product search, offers
new insights into decision tree partitioning.
