AGITB: A Signal-Level Benchmark for Evaluating Artificial General Intelligence

Current artificial intelligence systems exhibit strong performance on narrow tasks, while existing evaluation frameworks provide limited insight into generality across domains. We introduce the Artificial General Intelligence Testbed (AGITB), a complementary benchmarking framework grounded in twelve explicitly stated axioms and implemented as a suite of twelve automated, simple, and reusable tests. AGITB evaluates models on their ability to learn and to predict the next input in a temporal sequence whose semantic content is initially unknown to the model. The framework targets core computational properties, such as determinism, adaptability, and generalisation, that parallel principles observed in biological information processing. Designed to resist brute-force or memorisation-based strategies, AGITB requires autonomous learning across previously unseen environments, in a manner broadly inspired by cortical computation. Preliminary application of AGITB suggests that no contemporary system evaluated to date satisfies all test criteria, indicating that the benchmark provides a structured and interpretable means of assessing progress toward more general learning capabilities. A reference implementation of AGITB is freely available on GitHub.