# Beyond mimicry: a framework for evaluating genuine intelligence in artificial systems

## TL;DR

This paper introduces a new framework for evaluating genuine intelligence in AI systems by focusing on cognitive processes rather than just task performance.

## Contribution

The novel contribution is the mPCAB framework, which applies neurophysiological methods to assess consciousness-like processes in artificial systems.

## Key findings

- The mPCAB framework enables systematic comparisons across different substrates like digital and biological systems.
- Pilot studies show mPCAB's metrics are feasible and comparable across substrates.
- The framework addresses gaps in long-term reasoning, norm internalization, and transformational creativity.

## Abstract

Current AI benchmarks often equate mimicry with genuine intelligence, emphasizing task performance over the underlying cognitive processes that enable human-like understanding. The Machine Perturbational Complexity & Agency Battery (mPCAB) introduces a new, substrate-independent framework that applies neurophysiological methods used initially to assess consciousness in artificial systems. Unlike existing evaluations, it features four key components—perturbational complexity, global workspace assessment, norm internalization, and agency—that link mechanisms with functions. This enables systematic comparisons across digital, neuromorphic, and biological substrates, addressing three research gaps: long-term reasoning with coherent behavior, norm internalization amid distribution shifts, and transformational creativity involving meta-cognitive rule modification. By analyzing theories of consciousness (GNW, IIT, PP, HOT), we identify targets for AI implementation. Our cognitive architecture analysis maps human functions—such as working memory and executive control—to their computational counterparts, providing guiding principles for design. The creativity taxonomy progresses from combinational to transformational, with measurable criteria like changes in conceptual space and the depth of meta-level reasoning. Ethical considerations are integrated into frameworks for monitoring organoid intelligence, reducing bias in creativity, and addressing rights issues. Pilot studies demonstrate mPCAB’s feasibility across different substrates and show that its metrics are comparable. This framework moves evaluation away from superficial benchmarks toward mechanism-based assessment, supporting the development of mind-like machines and responsible AI advancements.