Theoretical framework demonstrates controlled structural unfolding in artificial cognitive systems, suggesting new foundations for governable machine-native intelligence.
General Structure Unfolding Intelligence (GSUI) develops a structural framework for studying intelligent processes as controlled unfolding from persistent structure. Its canonical form is expressed as +/- , with extensions involving context, triggering, unfolding space, control policy, evaluation, invariant preservation, certification, feedback, structural growth, and Structural Self. The repository examines Folding and Unfolding as complementary phases of an intelligence cycle: observation and experience may be folded into persistent structure, while that structure can later be unfolded into reasoning, generation, planning, routing, coding, control, action, and structural modification. GSUI distinguishes search within an existing possibility space from structural unfolding that can change future possibility itself. It introduces Unfolding Space to distinguish possible, reachable, allowed, preferred, selected, and certified transitions; treats the Control Plane as a first-class component of consequential intelligent unfolding; and defines Structural Growth as retained unfolding that changes future unfolding capacity. GSUI further introduces Structural Self as the persistent structural reference frame relative to which Delta, capability, continuity, world-model meaning, and future unfolding are defined. The framework explicitly separates Structural Self from consciousness or personhood, explores non-anthropomorphic machine perception and machine-native cognition, and maintains the governance principle that machine-native cognition should remain compatible with human-governable consequences. This v1.0.0 release establishes the foundational GSUI research framework, terminology, claim boundaries, visual language, and future research directions.
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Tan et al. (2026) studied this question.
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