  Great endeavors have been made to study AI's ability in abstract reasoning,
along with which different versions of RAVEN's progressive matrices (RPM) are
proposed as benchmarks. Previous works give inkling that without sophisticated
design or extra meta-data containing semantic information, neural networks may
still be indecisive in making decisions regarding RPM problems, after
relentless training. Evidenced by thorough experiments and ablation studies, we
showcase that end-to-end neural networks embodied with felicitous inductive
bias, intentionally design or serendipitously match, can solve RPM problems
elegantly, without the augment of any extra meta-data or preferences of any
specific backbone. Our work also reveals that multi-viewpoint with
multi-evaluation is a key learning strategy for successful reasoning. Finally,
potential explanations for the failure of connectionist models in
generalization are provided. We hope that these results will serve as
inspections of AI's ability beyond perception and toward abstract reasoning.
Source code can be found in https://github.com/QinglaiWeiCASIA/RavenSolver.
