2025/04/06 by Šprogar, Matej
#Artificial Intelligence (cs.AI) #D.2.8 #FOS: Computer and information sciences #I.2 #I.2.6 #I.5
paper · doi:10.48550/arxiv.2504.04430
Current AI systems demonstrate remarkable capabilities yet remain specialised, in part because no unified measure of general intelligence has been established. Existing evaluation frameworks, which focus primarily on language or perception tasks, offer limited insight into generality. The Artificial General Intelligence Testbed (AGITB) introduces a complementary benchmarking suite of fourteen elementary tests, with thirteen implemented as fully automated procedures. AGITB evaluates models on their ability to forecast the next input in a temporal sequence, step by step, without pretraining, symbolic manipulation, or semantic grounding. The framework isolates core computational invariants, such as determinism, sensitivity, and generalisation, that parallel principles of biological information processing. Designed to resist brute-force or memorisation-based strategies, AGITB enforces unbiased and autonomous learning. The human cortex satisfies all tests, whereas no current AI system meets the full AGITB criteria, demonstrating its value as a rigorous, interpretable, and actionable benchmark for evaluating progress toward artificial general intelligence. A reference implementation of AGITB is freely available on GitHub.