  Machine Learner for Automated Reasoning (MaLARea) is a learning and reasoning
system for proving in large formal libraries where thousands of theorems are
available when attacking a new conjecture, and a large number of related
problems and proofs can be used to learn specific theorem-proving knowledge.
The last version of the system has by a large margin won the 2013 CASC LTB
competition. This paper describes the motivation behind the methods used in
MaLARea, discusses the general approach and the issues arising in evaluation of
such system, and describes the Mizar@Turing100 and CASC'24 versions of MaLARea.
