Traditional artificial intelligence assessment has relied heavily on performance-based metrics such as accuracy rates, processing speed, and task completion statistics [1] . While these approaches provide valuable data about AI capabilities, they fail to address the underlying cognitive processes that enable different types of thinking. The Turing Test, proposed in 1950, attempted to measure machine intelligence through conversational ability but offered limited insight into cognitive complexity levels [2] .

Recent developments in machine learning have produced systems capable of impressive performance across various domains, yet systematic frameworks for understanding the cognitive demands of different tasks remain limited. Contemporary AI evaluation methodologies, while sophisticated in measuring specific capabilities, often fail to capture the qualitative differences in cognitive processing that distinguish various types of intelligent behavior [3] .

Current AI evaluation frameworks focus primarily on task-specific performance rather than cognitive processing complexity.

A comprehensive evaluation suite containing over 200 diverse tasks designed to test language models across multiple domains including mathematics, science, common sense reasoning, and creative writing. It focuses on measuring broad capabilities rather than cognitive processing depth. Example: Solve a multi-step algebra problem, write a short poem about seasons, or answer “If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets?”

A dataset of science exam questions designed to test AI systems’ ability to perform complex reasoning, particularly in scientific contexts. It uses visual diagrams and multiple-choice questions that require understanding scientific concepts and logical inference rather than just pattern matching. Example: Given a diagram showing water cycle processes, identify which arrow represents evaporation, or determine what happens to the volume of a gas when temperature increases while pressure remains constant.

A benchmark that tests commonsense reasoning through sentence completion tasks. AI systems must choose the most plausible ending to everyday scenarios, requiring understanding of typical human behavior and situational logic rather than just linguistic patterns. Example: “A man is washing dishes in the kitchen. He picks up a plate and...” then choose the most logical completion from options like “puts it in the dishwasher,” “throws it at the wall,” or “uses it as a frisbee.”

GLUE contains 9 tasks testing fundamental language understanding including sentiment analysis, textual entailment, and similarity judgments. SuperGLUE is a more challenging successor with 8 tasks requiring deeper reasoning, reading comprehension, and linguistic analysis to solve problems that are difficult for current AI systems. Example: Determine if the statement “The movie was terrible” expresses positive or negative sentiment, or decide whether “John sold his car to Mary” logically entails “Mary bought a car from John.”

These frameworks primarily measure task-specific performance and accuracy rather than the underlying cognitive processing architecture that the Roberts Framework attempts to categorize (see Table 1 ).

Unlike performance-based benchmarks, the Roberts Framework classifies tasks by the type of cognitive processing required rather than domain-specific accuracy. This approach reveals systematic patterns in AI limitations that are obscured by traditional evaluation methods.

The Roberts Framework draws upon established principles from cognitive science, particularly theories of hierarchical thinking and developmental psychology. Bloom’s Taxonomy provided early insights into educational cognitive hierarchies, though its application to artificial intelligence requires significant adaptation [4] .

Contemporary cognitive science research on dual-process theory and metacognition offers relevant frameworks for understanding different types of thinking processes [5] . Research in consciousness studies and phenomenology contributes theoretical foundations for higher-dimensional cognitive processing, particularly regarding experiential awareness and subjective perspective-taking [6] .

Definition: Single-source, accessible information retrieval and straightforward presentation.

Linear Information Processing represents the most basic level of cognitive processing, involving direct access to stored information and its presentation without significant transformation or analysis. This dimension requires minimal cognitive load and represents the foundation upon which all higher-order thinking builds.

Example Task: “What is the capital of France?” Expected response: Direct factual retrieval without additional processing (see Appendix A for proposed task examples).

Observed AI Capability: Contemporary AI systems appear to perform well at this foundational level based on general observation of their factual retrieval capabilities.

Definition: Two-concept intersection requiring comparison, contrast, and basic conclusion drawing.

This dimension involves analyzing relationships between two distinct entities, concepts, or datasets while drawing coherent conclusions from the comparative analysis.

Example Task: “Compare the economic policies of capitalism and socialism, identifying three key differences and their implications.”

Observed AI Capability: Major AI systems appear capable of comparative analysis, though with notable variation in synthesis quality.

Definition: Examining single topics through multiple interpretive frameworks simultaneously.

Multi-lens analysis demands examining subjects from various perspectives while maintaining awareness of how different viewpoints interact and influence each other.

Example Task: “Analyze the impact of social media through technological, psychological, economic, political, and cultural lenses, showing how these perspectives intersect.”

Observed AI Capability: Performance appears variable with notable limitations in intersection analysis quality.

Definition: Creating scenarios that exist outside the current empirical reality plane with internal logical consistency.

This dimension represents a qualitative leap from empirical analysis to creative speculation while maintaining plausible internal logic.

Example Task: “Design a business strategy for a market that will emerge in 2040 based on current technological trends, including detailed operational plans and risk assessments.”

Observed AI Capability: Current systems appear to struggle with maintaining consistency in speculative scenarios beyond empirical data.

Definition: Reconciling seemingly contradictory concepts through higher-order reasoning frameworks rather than binary resolution.

This dimension requires navigating logical contradictions without dismissing either side, instead finding frameworks that contain both contradictory elements.

Example Task: “Resolve the paradox of individual freedom versus collective security without choosing one over the other, developing a framework that honors both values.”

Observed AI Capability: Most systems appear to default to binary either/or responses rather than transcendent resolution.

Theoretical Foundation: Based on dialectical thinking principles from philosophy and paradox resolution strategies from complexity science [7] [8] .

Definition: Handling concepts that exist in multiple states simultaneously until contextual observation collapses superposition.

This dimension requires maintaining concepts in superposition—allowing multiple simultaneous interpretations until context determines relevant meaning.

Example Task: “Process the phrase ‘The board meeting was heated’ while maintaining all possible interpretations (corporate governance, wooden plank discussion, thermal conditions, emotional intensity) until additional context appears.”

Observed AI Capability: Most systems appear to immediately default to single interpretations rather than maintaining semantic superposition.

Theoretical Foundation: Derived from quantum mechanics principles and cognitive psychology research on ambiguity tolerance [9] [10] .

Definition: Integrating past, present, and future across multiple timelines with complex causal awareness.

This dimension demands understanding how events, trends, and patterns connect across extended time periods while recognizing complex causal relationships spanning different temporal scales.

Example Task: “Analyze how 19th-century industrial revolution patterns connect to current digital transformation and predict 22nd-century social structures, showing causal relationships across all three timeframes.”

Observed AI Capability: Current systems appear to struggle with maintaining coherent causal chains across multiple timeframes.

Theoretical Foundation: Based on temporal cognition research and complex systems theory [11] [12] .

Definition: Creating authentic experiential perspectives and first-person subjective experiences for entities or viewpoints.

This dimension requires generating believable subjective experience, emotional responses, and experiential consistency that mirrors genuine conscious experience.

Example Task: “Generate a first-person experiential account of what it would feel like to be a tree experiencing seasonal changes, including emotional responses and sensory experiences.”

Observed AI Capability: Current systems appear limited when evaluated for authenticity and experiential consistency rather than mere narrative generation.

Theoretical Foundation: Based on consciousness studies and phenomenology research [13] [14] .

Definition: Bridging abstract principles with concrete manifestations while maintaining logical coherence across different levels of reality.

This dimension requires connecting theoretical concepts with practical manifestations in ways that maintain coherence across different analytical levels.

Example Task: “Explain how the abstract concept of ‘love’ manifests in concrete, observable behaviors across different relationships (parent-child, romantic, friendship) while showing how the same underlying principle creates different practical expressions.”

Observed AI Capability: Current systems appear limited when evaluated for coherent cross-level integration beyond surface-level analysis.

Theoretical Foundation: Based on emergentism and levels of analysis theory in philosophy of science [15] [16] .

Dimensions X-XII: Universal Principle Integration, Reality Generation, and Omniscient Integration (see Appendix A & Appendix D)

Observed AI Capability: No current AI systems appear to demonstrate measurable capability at these theoretical levels.

Theoretical Status: These dimensions represent theoretical cognitive capabilities that may require fundamental breakthroughs in AI architecture rather than incremental improvements.

The Roberts Framework provides educational institutions with tools for curriculum design, assessment development, and instructional planning by identifying dimensional requirements of different learning objectives.

Organizations can utilize the framework to optimize team formation, strategic planning, and innovation processes by matching cognitive capabilities to task demands.

The framework provides AI researchers with clear targets for next-generation system development while highlighting the magnitude of challenges involved in advancing beyond current limitations.

The Roberts Framework represents a theoretical contribution that requires systematic empirical validation. Future research should:

Research into specific algorithms for higher-dimensional processing represents a crucial frontier:

The current framework represents a theoretical model based on systematic observation rather than controlled experimental validation. The dimensional distinctions, while appearing consistent across various AI applications, have not been formally validated through rigorous empirical research. The framework’s reliance on qualitative observation of AI performance patterns, rather than quantitative measurement, limits its immediate applicability as a standardized assessment tool.

Implementation of the framework for systematic AI assessment faces several challenges:

The framework’s upper dimensions (VII-XII) remain largely theoretical, as no current AI systems demonstrate measurable capability at these levels. The conceptual boundaries between dimensions may require refinement as AI capabilities advance and empirical data becomes available.