The premise of this study was the assumption that the amount of information transmitted using Psi may be limited by the nature of the bias amplifier. It has already been suggested earlier that the generation of geographic coordinates in MMI by the Binary Word method is hampered by the fact that Psi can transmit no more than 1 bit of information per second. In this case, it can be assumed that an increase in the amount of entropy will not lead to an increase in the amount of information, but will only increase the accuracy of its transmission.

However, it is worth noting that all currently known methods of bias-amplification are based on entropy compression from N input bits to 1 output bit. The very nature of such compression does not imply that information more than 1 bit can pass it without loss. Of course, if the coordinate generation algorithm is based on a random walk, the information can be accumulated and the coordinate can be obtained in several queries. However, is it possible to carry out such entropy compression, in which the output would be not 1 bit of information, but, for example, 32 bits?

To implement such compression, a slightly different approach to amplification is required, assuming that the entropy sequence is statistically dominated not by a certain bit value, but by a certain bit pattern. There are two obvious problems here:

- A pattern can occur with a non-constant interval, which means that if we feed a fixed number of bits to the amplifier input, a significant proportion of the patterns will be â€ścut into piecesâ€ť by the batch treshold.
- The pattern may be transmitted with errors and the sequence will contain many â€śmutationsâ€ť of the same pattern with differences of 1-2 bits.

We tried to solve the first problem using the â€śrotationâ€ť algorithm. In this algorithm, the bit stream passes through a 32-bit window, shifting 1 bit per step. At each step, a new number is obtained from 32 bits in the window. Thus, an array of all possible numbers obtained by the Binary Word method, regardless of the size of the intervals between them. In the simplest version of the amplifier, the output is a number that occurs more often than others.

However, this approach introduces a â€śregularity problemâ€ť where numbers that have a highly regular bit pattern, such as 1010101010, are more likely to be duplicated when bitshifted.

The second problem concerning errors in patterns is solved in two ways. The first is to simply sort through all the found numbers and calculate how many analogues exist for each of them with a difference of 1-2 bits. This method can be difficult to calculate because it involves many nested loops. The second method is to convert all numbers to Gray codes and place their values â€‹â€‹on a one-dimensional scale, and then find on this scale the point around which the density of values â€‹â€‹is maximum.

The disadvantage of this method is that it only works if input bit sequences do not intersect with each other.

When trying to combine the rotation method with the similarity search method, strong autocorrelation biases are found that cannot be eliminated while analogs are searched among numbers that have mutual bits. To date, this problem has not been resolved.