  In this study, we continue our exploration of the concept of information
temperature as a characteristic of random sequences. We describe methods for
introducing the information temperature in the context of binary high-order
Markov chain with step-wise memory and investigate the application of the
temperature as a parameter of the sequence complexity. We aim to define
complexity based on the derivative of entropy with respect to information
temperature, drawing an analogy to thermodynamic heat capacity. The maximum
complexity of a random sequence is achieved when its information "heat
capacity" approaches its highest possible value, which is directly influenced
by the sequence memory depth. We also discuss the potential of utilizing
information temperature as an indicator of the intellectual level exhibited by
any text-generating agent.
