PotARCin Measures the Soyut Mental Execution Ability of Artificial Intelligence Models

Artificial intelligence researchers rely on Abstraction and Reasoning Corpus (ARC) test for a long time to measure the level of intelligence of models. However, the current ARC rating remains in a rather narrow frame: it only controls whether it produces and produce the right output grid for test input. The researchers argue that this simple approach ignores various capabilities with artificial intelligence systems.

PotARCin is a multi-dimensional expansion of the ARC test. The new benchmark focuses on:

• Not only the correct result production, but evaluates intermediate steps and logical transitions in the process of problem solving
• Measures how much the patterns found by the model can be improved
• Testing abstract reasoning capacity in different difficulty levels
• Detailed analysis of the error forms of the model and weak points
• distinguishes simple pattern matching between real abstracting skills

PotARCin offers a richer data set and a multi-layer assessment methodology to understand whether artificial intelligence systems are really capable of making abstract thinking. This approach aims to reveal whether models follow simple contextual patterns and perform in-depth conceptual learning.

Research shows that standard benchmarks such as ARC are insufficient in the AI’s intelligence level assessment and require multi-dimensional criteria. PotARCin opens the way to make more realistic and comprehensive AI assessment in the future.