loading page

KitBit: A New AI Model for Solving Intelligence Tests and Numerical Series
  • José Manuel Gilpérez Aguilar ,
  • Victor Corsino ,
  • Luis Herrera
José Manuel Gilpérez Aguilar
Universidad de Castilla La Mancha - UCLM

Corresponding Author:[email protected]

Author Profile
Victor Corsino
Universidad de Castilla La Mancha - UCLM
Author Profile
Luis Herrera
Author Profile


The resolution of intelligence tests, in particular numerical sequences, has been of great interest in the evaluation of AI systems. We present a new computational model called KitBit that uses a reduced set of algorithms and their combinations to build a predictive model that finds the underlying pattern in numerical sequences, such as those included in IQ tests and others of much greater complexity. We present the fundamentals of the model and its application in different cases. First, the system is tested on a set of number series used in IQ tests collected from various sources. Next, our model is successfully applied on the sequences used to evaluate the models reported in the literature. In both cases, the system is capable of solving these types of problems in less than a second using standard computing power. Finally, KitBit's algorithms have been applied for the first time to the complete set of entire sequences of the well-known OEIS database. We find a pattern in the form of a list of algorithms and predict the following terms in the largest number of series to date. These results demonstrate the potential of KitBit to solve complex problems that could be represented numerically.
01 Nov 2023Published in IEEE Transactions on Pattern Analysis and Machine Intelligence volume 45 issue 11 on pages 13893-13903. 10.1109/TPAMI.2023.3298592