  In this work, we prove over 3000 previously ATP-unproved Mizar/MPTP problems
by using several ATP and AI methods, raising the number of ATP-solved Mizar
problems from 75\% to above 80\%. First, we start to experiment with the cvc5
SMT solver which uses several instantiation-based heuristics that differ from
the superposition-based systems, that were previously applied to Mizar,and add
many new solutions. Then we use automated strategy invention to develop cvc5
strategies that largely improve cvc5's performance on the hard problems. In
particular, the best invented strategy solves over 14\% more problems than the
best previously available cvc5 strategy. We also show that different
clausification methods have a high impact on such instantiation-based methods,
again producing many new solutions. In total, the methods solve 3021 (21.3\%)
of the 14163 previously unsolved hard Mizar problems. This is a new milestone
over the Mizar large-theory benchmark and a large strengthening of the hammer
methods for Mizar.
