NexusAI: Enabling Design Space Exploration of Ideas through Cognitive Abstraction and Functional Decomposition

2026-04-12

AI Summary

This study addresses two key challenges in large language model–mediated creative generation: premature user fixation on suboptimal ideas due to loosely structured outputs, and the lack of fine-grained combinatorial control—termed “combinatorial opacity”—in existing tools. To overcome these limitations, the authors propose a computational pipeline called Cognitive Abstraction, which transforms raw generative outputs into a navigable, transformable design space through functional decomposition, multi-level abstraction, and cross-dimensional recombination. Integrated into the NexusAI system, this approach enables effective human-AI collaborative exploration. The work formally conceptualizes combinatorial opacity as a critical barrier in creative collaboration and introduces a scalable framework of cognitive primitives for creative operations. A user study (N=14) demonstrates that NexusAI significantly enhances exploratory breadth, reduces cognitive load, and facilitates perspective reframing compared to baseline systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Large Language Models (LLMs) offer vast potential for creative ideation; however, their standard interaction paradigm often produces unstructured textual outputs that lead users to prematurely converge on sub-optimal ideas-a phenomenon known as fixation. While recent creativity tools have begun to structure these outputs, they remain compositionally opaque: ideas are organized as monolithic units that cannot be decomposed, abstracted, or recombinable at a sub-idea level. To address this, we propose Cognitive Abstraction (CA), a computational pipeline that transforms raw LLM-generated inspiration into a navigable and transformable design space. We implement this pipeline in NexusAI, a prototype diagramming system that supports (I) decomposition of inspiration into typed functional fragments, (II) multi-level abstraction to externalize mental scaling, and (III) cross-dimensional recombination to spark novel design directions. A within-subject user study (N=14) demonstrates that NexusAI significantly improves design space exploration, reduces cognitive overhead, and facilitates perspective reframing compared to a baseline. Our work contributes: (1) a characterization of "compositional opacity" as a barrier in human-AI co-creation; (2) the CA pipeline for operationalizing creative cognitive primitives at scale; and (3) empirical evidence that structured, multi-level representations can effectively mitigate fixation and support divergent exploration.

Problem

• compositional opacity
• fixation
• design space exploration
• creative ideation
• human-AI co-creation

Innovation

• Cognitive Abstraction
• Functional Decomposition
• Design Space Exploration
• Compositional Opacity
• LLM-based Ideation