Research guide
What Is ASI? Artificial Superintelligence and Structural Autonomy
ASI stands for Artificial Superintelligence. Classical accounts emphasize capability beyond the best humans across a broad range of important intellectual domains. COG's KSC2 research adds a structural perspective: how can progress in forming high-level problem structures be studied before ASI is achieved?
This guide introduces COG's research perspective. Consult the original papers for complete arguments, conditions and references.
Structural exploration in open problems
What is the difference between AGI and ASI?
Within KSC2 and the engineering definition of AGI, AGI aims at broad autonomous coverage of intellectual work that ordinary human teams can complete. Complete ASI additionally requires autonomy across a broad range of highly structurally open tasks, entering areas historically dependent on the most capable humans for reframing, abstraction discovery and original structure generation, at or beyond the highest human level. The two can be interacting capability frontiers rather than strictly sequential stages.
What are structural openness and structural autonomy?
Structural openness describes the task: how much of its necessary problem representation, abstraction, key variables and exploration directions is not specified in advance. Structural autonomy describes the solver: whether AI can form, evaluate and revise these high-level structures without critical human structural input during execution. A task requirement and a system capability must not be treated as the same measure.
How do KSC1, KSC2 and KSC3 relate?
KSC1 provides functional coordinates for knowledge, structure generation and constraint maintenance. KSC2 extends the account of structure generation toward a progressive structural framework for ASI. KSC3 examines how structural experience, external selection and training influence later models' initial tendencies. These are a foundation and two research extensions, not three chronological stages of intelligence.
Does AACC directly measure ASI?
AI-Abstraction Cognitive Compounding describes how AI expands, develops and validates valuable structures, producing knowledge and candidate structures that expand subsequent exploration. It is not an independent ASI capability metric. Likewise, KSC2's structural framework is not a complete distance function for ASI; assessment still requires explicit task protocols and empirical testing.
Original papers and DOIs
KSC2: A Progressive Structural Framework for ASI Structural Openness, Structural Autonomy, and the Structural Difference between AGI and ASI
Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI) are commonly described in terms of capability coverage and performance relative to humans. Existing work has already sought to characterize AGI not as a single end…
DOI: 10.5281/zenodo.21976747KSC: Knowledge, Structure, and Constraint — A Functional Framework for Human–AI Differences and Collaboration
Current AI systems can draw on broad bodies of knowledge and rapidly generate complex structures, yet their capabilities still vary substantially across tasks. Some problems are constrained mainly by the information available; some require …
DOI: 10.5281/zenodo.21991838An Engineering Definition of AGI: Complex-Project Closed-Loop Capability as an Assessment Criterion
Artificial general intelligence (AGI) has long lacked a stable and operational definition. Existing definitionsvariously emphasize human-like behavior, subjective mind, broad cognitive ability, human-levelperformance, economic value, or soc…
DOI: 10.5281/zenodo.22803294KSC3: From Distributional Structural Internalization to Transgenerational AI Evolution — The DSI and ES–DSI Mechanisms
KSC abstracts intelligent activity into three fundamental variables: Knowledge (K), Structure generation (S), and Constraint maintenance (C). Existing KSC work describes K as the knowledge and crystallized structures available to the curren…
DOI: 10.5281/zenodo.21993013