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

Abstract: Despite major advances in machine learning, current artificial intelligence systems continue to fall short of human-like general intelligence. While LLMs can generate fluent and coherent outputs, they lack the deep understanding and adaptive reasoning that characterize truly general intelligence. Existing evaluation frameworks, which are centered on broad language or perception tasks, fail to capture generality at its core and offer little guidance for incremental progress. To address this gap, this paper introduces the artificial general intelligence testbed (AGITB), a novel and freely available benchmarking suite comprising twelve fully automatable tests designed to evaluate low-level cognitive precursors through binary signal prediction. AGITB requires models to forecast temporal sequences without pretraining, symbolic manipulation, or semantic grounding. The framework isolates core computational invariants - such as determinism, sensitivity, and generalization - that align with principles of biological information processing. Engineered to resist brute-force and memorization-based approaches, AGITB presumes no prior knowledge and demands learning from first principles. While humans pass all tests, no current AI system has met the full AGITB criteria, underscoring its potential as a rigorous, interpretable, and actionable benchmark for guiding and evaluating progress toward artificial general intelligence.