I'm still not sure if it's best to store 2, 3 or 4 bits of info per square.
The problem with 4 bits/sq (hence 32 bits per ray) is that the LUT would be too large to fit into RAM, but unlike in 2006, it would be small enough to fit onto an SSD, and it could probably be hashable in RAM, as only a small part of it would be needed in the search from a certain position.
Perhaps I will revisit this idea at some point. Additional incentive is what I've done in Rodent IV. It calculates two different, competing piece/square table scores for the entire board, and then does weighted average of them, pulling result towards better of the scores (more so if score difference is bigger). This is of course not as robust as a full-blown neural network, but still a neat way to add some form of second-order non-linear scoring.
The Chinese chess paper (section V.B) reports that, in a match between trained EVAL0 versions, the one with tapered eval (which, in that case, interpolated between the opening and the endgame) beat the one without convincingly, with a 62.75% winrate, but of course, the tapering coefficient doesn't have to only depend on the phase of the game, and even further gains seem possible if there are >2 competing scores like in Winter (which currently has 4, as regulated by the kNumClusters constant).
Just to be clear, Rodent IV code compares competing piece/square table scores which already take into account game phase. Winter does something else (probably something great).
