[tasuki]

I'd argue it's clear why it's hard for a neural net to figure out.

A ladder is a kind of a mechanical one-way sequence which is quite long to read out. This is easy for humans (it's a one-way street!) but hard for AI (the MCTS prefers to search wide rather than deep). It is easy to tell the neural net as one of its inputs eg "this ladder works" or "this ladder doesn't work" -- in fact that's exactly what KataGo does.

See the pictures for more details about ladders: https://senseis.xmp.net/?Ladder

[dragontamer]

Doesn't MCTS deeply AND broadly search though?

Traditional MCTS searches all the way to endgame and estimates how the current position leads to either win or loss. I'm not sure what the latest and greatest is but those % chance to win numbers are literally a search result over possible endgames IIRC.

I guess I'd assume that MCTS should see ladders and play at least some of them out.

[immibis]

I don't know that much about MCTS, but I'd think that since a ladder requires dozens of moves in a row before making any real difference to either player's position, they just don't get sampled if you are sampling randomly and don't know about ladders. You might find that all sampled positions lead to you losing the ladder, so you might as well spend the moves capturing some of your opponent's stones elsewhere?

[tasuki]

The short ones, sure. The long ones, it's hard for pure MCTS to... keep the ladder straight?