Inference-agnostic intelligence measurement framework. Meta-research and novel benchmarks for AI beyond text generation.
The Universal Intelligence Benchmark (UIB) proposes an eight-dimensional, cost-normalized framework for measuring intelligence across diverse AI systems. This article operationalizes the second UIB dimension — Embodied Intelligence (Dembodied) — defining it as the capacity for intelligent behavior arising from physical interaction with an environment, encompassing spatial reasoning, physics und...
The Universal Intelligence Benchmark (UIB) theoretical framework, dimensional taxonomy, and composite scoring formula have been developed across nine preceding articles in this series. This article completes the framework by presenting the UIB Open-Source Benchmark Suite — the concrete evaluation infrastructure that operationalizes those concepts for independent replication. We address three re...
The Universal Intelligence Benchmark (UIB) has systematically developed eight intelligence dimensions over the course of this series: causal reasoning, embodied grounding, temporal planning, social cognition, resource efficiency, linguistic reasoning, multimodal perception, and meta-learning. This article presents the mathematical framework for integrating these dimensions into a single UIB Com...
Intelligence measurement stands at a critical inflection point. The accelerating saturation of static benchmarks — with median time-to-saturation declining from five years in 2019 to under one year by 2025 — demands a fundamental rethinking of how we evaluate artificial intelligence. This article projects the evolution of AI evaluation paradigms over the next decade (2026-2035), analyzing three...
Open-source benchmark frameworks have become the backbone of AI model evaluation, yet none provides simultaneous coverage of multidimensional intelligence measurement, inference cost normalization, and cryptographic reproducibility certification. This article presents the architecture and design rationale for the Universal Intelligence Benchmark (UIB) open-source suite, a modular evaluation fra...
Current artificial intelligence benchmarks measure isolated capabilities — reasoning, coding, knowledge retrieval — yet no single metric captures the multidimensional nature of machine intelligence. This article presents the Universal Intelligence Benchmark (UIB) Composite Score, integrating eight previously defined intelligence dimensions (reasoning, causal, temporal, social, efficiency, trans...
As large language models approach ceiling performance on standard benchmarks, the question shifts from "how smart is this model?" to "how smart is this model per unit of resource consumed?" This article proposes the UIB-Efficiency dimension — a resource-normalized intelligence score that integrates accuracy with computational cost, energy consumption, memory footprint, and inference latency. We...
Current AI evaluation overwhelmingly focuses on individual cognitive tasks — reasoning, coding, mathematics — while neglecting the social and collaborative capabilities that define human intelligence in practice. This article introduces the UIB-Social dimension, a formal evaluation framework for measuring social intelligence in large language models across four sub-dimensions: Theory of Mind (T...
Temporal reasoning and long-horizon planning represent perhaps the most consequential gap between current large language models and human cognitive capability. While frontier models achieve near-human performance on short planning tasks (under 15 steps), their accuracy degrades catastrophically beyond 25 planning steps — a phenomenon we term the horizon collapse. This article examines three res...
Current intelligence benchmarks evaluate AI systems as disembodied reasoners operating on text, images, and symbolic tasks detached from physical reality. This article introduces Embodied Intelligence as a formal dimension within the Universal Intelligence Benchmark (UIB) framework, arguing that any comprehensive measure of machine intelligence must assess a system's capacity for sensorimotor g...