Leading term strandings for webs

What the math gives to ML

The paper gives a constructive way to turn colored curve systems into valid weighted trivalent graphs while preserving a prescribed lexicographically leading tensor monomial. The transferable asset is not the specific web representation, but the combination of discrete conservation laws, monotone color ordering, and canonical leading-term routing: local rewrites produce globally valid sparse computations without arbitrary learned connectivity. This suggests a structured tensor or attention layer whose paths carry discrete feature channels, whose edge widths are determined by conserved channel weights, and whose routing is biased toward a unique monotone normal form. The resulting module could provide sparse, interpretable connectivity and a useful regularizer against routing crossings and cancellations.