KSC3: From Generated Structures to Inherited Knowledge — Transgenerational Capability Accumulation in AI

Abstract

The KSC framework abstracts the resources underlying intelligent activity into three basic variables: Knowledge (K), Structure generation (S), and Constraint maintenance (C). Extending the K axis into the temporal dimension, this paper examines a foundational question: can candidate structures that current-generation AI helps generate through S/C pass through validation, selection, retention, and inheritance, acquire the status of K in subsequent AI systems, and thereby produce transgenerational capability accumulation? This paper argues that high-quality K should not be understood only as a static collection of facts. It can also be understood as reusable results formed by compressing historical cognitive search. Solving a problem for the first time may require extensive search, reasoning, trial and error, and constraint maintenance. Once an effective structure produced through that process is reliably inherited, subsequent systems can use recognition, retrieval, and local adaptation to avoid repeating online search on a comparable scale, thereby changing the range that must actually be explored for related tasks. On this basis, the paper proposes the following cross-temporal KSC relation: **K_t → S_t/C_t → ΔStructure_t ⇒ K_{t+1}** Here, **⇒** indicates that a generated structure acquires the status of K at the next stage only after it has been validated, selected, retained, and effectively inherited by subsequent systems. On this basis, the paper distinguishes two forms of capability: **native capability**, observed after controlling for existing reusable structures directly relevant to the target task; and **effective capability**, observed in real tasks after relevant K has been inherited and can be accessed and invoked. Native capability here does not mean “capability without knowledge.” It refers to a system’s capacity for structure generation and constraint maintenance on the basis of its general knowledge. Even if growth in native S/C is limited, effective capability may still improve as high-quality, accessible, and reusable task-relevant K continues to accumulate. Software engineering, with its combination of large-scale structure generation, strong external validation, and high-fidelity preservation of engineering artifacts, is a candidate domain for observing this mechanism. Finally, the paper proposes two empirical testing paths: cross-generational differences between tasks with mature historical structures and genuinely novel-rule tasks can serve as a supportive empirical pattern; under otherwise approximately constant conditions, interventions on the availability of relevant historical K can more directly test K’s contribution to effective capability. This paper does not claim that all AI capability growth comes from K, nor does it equate transgenerational structural inheritance with autonomous recursive self-improvement. It establishes only a weaker theoretical proposition: **the conditions already exist for AI to participate in generating structures that later AI systems can inherit, and once high-quality structures acquire the status of K, they can become a distinguishable source of effective capability growth in addition to improvements in native S/C.**