🤖 AI Summary
Current large language models lack a perceptible sense of “mindedness” in extended dialogue, often appearing flat and devoid of inner life. This work proposes a “dimensional integrity” framework centered on four first-person behavioral stances—time, truth, entropy, and love—grounded in empirical evidence of human cognition, complemented by observable behavioral layers such as proactivity and conversational rhythm. Rather than prioritizing task performance, the framework enhances the perceived mindedness of artificial interlocutors. It shifts the focus of credible AGI development from capability to dimensional integrity, clearly distinguishing perception engineering from theories of machine consciousness. A prototype implementing temporal dimensionality through behavioral modeling and rhythm control is presented, alongside six falsifiable predictions supporting preregistered experiments. Key behavioral features have already been deployed in a production-level companion application.
📝 Abstract
Large language models are broadly capable, yet in sustained one-to-one conversation they still read as flat: competent, responsive, and somehow not quite the presence of a mind. We hypothesize that a central missing ingredient is not more capability but dimensional completeness. We propose that the believability of an artificial interlocutor -- the degree to which a user attributes an inner life to it, which we call perceived mind -- is governed by whether the agent expresses a small set of first-person stances that humans use as evidence of mind, and that this is separable from task intelligence. We name four such dimensions -- time, truth, entropy, and love -- each defined as a behavioral stance rather than a benchmark competency, each with a human analog and a concrete emulation path; the time dimension already has an author-reported prototype. We identify an observable behavior layer -- initiative (unprompted action) and cadence (the shape and timing of turns) -- through which the stances surface in conversation, both partially realized as deployed features in a production companion application. We state six falsifiable predictions that a later pre-registered study will test, separating those that are pre-registrable now from those that remain conjectures pending operationalization. This is a conceptual framework: it reports no human-subjects data, and its central comparative claims are predictions, not findings. Throughout we hold a firm boundary -- the object is inferrable interiority, not interiority; this is perception engineering, not a theory of machine consciousness -- and we treat the resulting attachment and manipulation risks as load-bearing rather than incidental.