COG / PAPERS
Formal, stable, and citable definitions, methods, and research arguments.
Knowledge · Structure · Constraints
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.22803294Current 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.21991838Artificial 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 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.21993013Current progress in AI capabilities is commonly explained in terms of scaling, improvements in training algorithms, self-training, and inference-time compute. Yet software engineering exhibits a phenomenon that warrants separate study: even…
DOI: 10.5281/zenodo.22124999A model of AGI progress using the scale axis, paradigm axis, and engineering axis, with the threshold of stable complex-task delivery as the key milestone.
Generative AI can already produce code and functionality at low cost, yet the reliable delivery of complex software still depends on continuously managing task constraints, component dependencies, change impacts, and validation feedback. Th…
DOI: 10.5281/zenodo.21917081Large language models and their agents can already handle some real-world software engineering tasks, yet a single successful execution is still insufficient to demonstrate that a specific result has reached a level at which it can be deliv…
DOI: 10.5281/zenodo.21959920Software engineering evaluation needs a continuing supply of tasks that reflect real development, distinguish system capabilities, and remain affordable to construct. This paper proposes MRAC Bench, an evaluation method and infrastructure b…
DOI: 10.5281/zenodo.22790766Previous work introduced Multi-Round Audit Convergence (MRAC), which evaluates whether a system can stably handle a current task by tracking how issues change across repeated “audit–remediation–re-audit” cycles on the same engineering objec…
DOI: 10.5281/zenodo.21917630Large language models and their Agent systems can already generate code, plan tasks, invoke tools, and modify real engineering states, yet growth in capability has not been matched by corresponding improvements in the reliable delivery of c…
DOI: 10.5281/zenodo.21931156