That just suggests the example problems being solved are effectively getting simpler as n,m increase, no?
I haven’t read the papers, but I’m not following how you construct a network flow where solving a max flow / min cut gives you a subset of txs that maximises f_C - \lambda s_C for a given \lambda. The DeepSeek approach seems like it could solve for a C that gives the largest feerate, by bisecting on different values for \lambda, but that seems more like finding the breakpoints in order?
If you have txs A at f/s = 100/1, B at 3980/50, C at 920/49 (with \lambda=5000/100=50), and where C spends B and B spends A, what’s the flow diagram that tells you the first/best breakpoint is AB vs C, rather than A vs BC?