CTM-AI: A Blueprint for General AI Inspired by a Model of Consciousness

arXiv cs.AI / 5/7/2026

💬 OpinionIdeas & Deep AnalysisModels & Research

共有:

Key Points

The paper argues that current AI remains “narrow” and lacks the flexible, adaptive, multisensory intelligence associated with human generality, motivating new architectures inspired by theories of consciousness.
It introduces CTM-AI, an early blueprint that combines a formal Conscious Turing Machine (CTM) model of consciousness with modern foundation models.
CTM-AI uses a large set of processors spanning specialized expert modules (such as vision-language models and APIs) and general-purpose learners that can develop new skills.
The system selects, integrates, and exchanges information across processors to address each task, and it reports strong results on MUStARD and UR-FUNNY as well as large gains on tool-using/agentic benchmarks like StableToolBench and WebArena-Lite.
Overall, the authors position CTM-AI as a principled and testable approach toward general AI inspired by a consciousness framework.

Abstract

Despite remarkable advances, today's AI systems remain narrow in scope, falling short of the flexible, adaptive, and multisensory intelligence that characterizes human capabilities. This gap has fueled longstanding debates about whether AI might one day achieve human-like generality or even consciousness, and whether theories of consciousness can inspire new architectures for AI. This paper presents an early blueprint for implementing a general AI system, CTM-AI, combining the Conscious Turing Machine (CTM), a formal machine model of consciousness, with today's foundation models. CTM-AI contains an enormous number of powerful processors ranging from specialized experts (e.g., vision-language models and APIs) to unspecialized general-purpose learners poised to develop their own expertise. Crucially, for whatever problem must be dealt with, information from many processors is selected, integrated, and exchanged appropriately to solve the task. CTM-AI achieves state-of-the-art accuracy on MUStARD (72.28) and UR-FUNNY (72.13), outperforming multimodal and multi-agent frameworks. On tool-using and agentic tasks, CTM-AI achieves 10+ points of improvement on StableToolBench and WebArena-Lite. Overall, CTM-AI offers a principled, testable blueprint for general AI inspired by a model of consciousness.