Lemma 2.1 says 'if this assignment exists then X'
Then later in the proof you say 'here is such an assignment, so, applying lemma 2.1, therefore X'
You don't need to assume the existence of the assignment, you prove that if the assignment exists then something else follows, and then later if you can find that assignment then you get the result of lemma 2.1.
With the Erdős proof, OpenAI added perspectives from working mathematicians that gave some context -- hope something like that appears for this one eventually.
Maybe someone should ask the model to make a more clearly written and thus easy to verify proof :)
I'm not sure why you find this proof so hard to read. I found it mostly quite readable (and the definition of L is straightforward? I wouldn't have written it quite that way but it's hardly inscrutable), although I feel like some parts are maybe lacking some exposition to explain the reason for certain things -- it doesn't feel written "in order". I also don't like that it's not cleanly separated into theorems and proofs -- some of the proof occurs in parts that aren't set off, for instance, and there isn't even a proper main theorem statement! But overall I was able to get through it without a lot of trouble and I'm not even a graph theorist...
L(t, epsilon)_e breaks down the range of L onto its component values indexed by edge, but this only really makes sense when you know that t and epsilon are. They are sort of defined in the middle of a sentence in the proof of 2.1, which IMO is asking a lot of the reader, and this sort of sloppiness is a way that errors can hide in a proof. (Not that I see an error here. But a formalization in Lean or whatever would not get away with this.)
And, in the same definition of L, for some reason the e=uv part comes at the end only after u and v are used.
What would be wrong with stating, in the definition, what sorts of objects t and epsilon are and with omitting e entirely in favor of just calling the edge uv everywhere?