A few comments.
1) As I pointed out in the mirror go topic, single board komi does not matter, since it is negative on the mirror board, and komi - komi = 0. Komi in the difference game must be zero, as the second player is satisfied with jigo. (I suppose that you could give the second player komi of ½, but that seems unnecessary.)
A few years ago one of the go AI developers lamented a loss with a komi of 6½ (under territory scoring) because the bot had been trained with a 7½ pt. komi. I understood the problem, and I also understood training for the specific conditions of contest. But for myself I would want a more general and robust program. Like we have now with KataGo.
As for the use of neural nets for difference games, it may be OK to use neural nets that have been trained on a specific komi on each board, The top module might use information from both nets to choose the next play. How well such an architecture would work is, OC, an open question.
2) Although, as we have seen, the margin of victory on a single board matters, but it does not matter in the difference game. So a program could play the difference game using MCTS, which does not care about the margin of victory. And even so we would be evaluating plays on a single board based upon the margin of victory.
3) Because each board mirrors the other before the two plays to be compared are made, the difference game is relatively impervious to imbalance on a single board. High handicap games are thus not problematic. OC, bots like KataGo can handle high handicaps by estimating territory. But the difference game does not require such an estimate, because of the mirroring. Unless one of the plays being compared is really bad, the difference game should normally be close. Besides, the margin of victory does not matter.
4) Difference games could be useful for whole board problems, such as Igo Hatsuyoron #120. This is not to take anything away from the wonderful work and results of Cassandra and other humans, and that of lightvector training KataGo. But the ability to focus the question on comparing two specific plays in a specific position, especially positions identified as critical, could be used to check and validate those results or to suggest other options. Difference games in themselves are too specific to solve problems or play games, but they provide a strong analytical tool.
5) What about training? My hope is that already trained neural nets could be used for each board without specific training for difference games. OC, training for difference games is almost certainly better, but by how much?
6) What about efficiency? We can compare two plays with a top bot simply by making each play and comparing results. But the bots are built to win games, not compare plays. Difference games are designed to compare plays and positions. But there is a large performance hit from doubling the search space. In practice that may be a big drawback.
7) What about accuracy? Simply having to win or not lose a difference game reduces the labor of finding a proof by orders of magnitude of that of finding perfect play. However, for boards of size 9x9 or larger, I think that doubling the search space dwarfs that reduction. That also means that we shall probably be left with statistical indications. Suppose, for instance, that move A is superior to move B, and that Black makes move A. Then we may well have a winrate for Black playing first greater than 99% with hundreds of thousands of playouts. But when White plays first maybe the optimal result is jigo. Then we might well have a winrate for White playing first of around 40% with the same number of playouts. That is a very weak indication that White should lose, playing first.
This may still provide a better indication than simply making each play on a single board. Then we might get a winrate for A of 45% versus a winrate for B of 37%.
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Bill Spight
wins on the right hand board, then 
makes seki on the left hand board. By area scoring Black has 9 pts. on the right and 5 pts. on the left, and White has 2 pts. on the left. Black wins on the combined boards by 12 pts. By territory scoring Black has 9 pts. on the right plus 0 or 2 pts. on the left, depending on whether you count points in seki. Black wins on the combined boards by 9 or 11 pts. So Black to play wins the difference game.
wins on the right hand board, then 
wins on the left. Each player gets the same number of points, for jigo.
wins on the left. The result is jigo by area scoring, Black +1 by territory scoring. In any event, White to play cannot win the difference game. So 
is a better play than 
. QED.