KSC3: From Distributional Structural Internalization to Transgenerational AI Evolution — The DSI and ES–DSI Mechanisms

Abstract

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 current task, but a deeper question remains: for generative models, how do such structures enter K, and how do they subsequently influence S? This paper proposes **Distributional Structural Internalization (DSI)** as a further interpretation of K. For structural knowledge, model capability is not well described as a binary state of “knowing” or “not knowing.” When facing a particular problem, multiple existing problem representations, relations, and solution structures may participate in subsequent generation with different initial tendencies. DSI focuses specifically on problem-solving structures that can transfer across surface-level instances: how the statistical distribution of such structures in the training environment is transformed into generative tendencies when a model encounters new but structurally related problems, so that the initial candidate-structure distribution of S already assigns different weights to different structures. DSI is therefore not a general renaming of the idea that “models learn the training distribution,” but an abstraction of the functional relationship by which **distributions of structural experience give rise to transferable initial structural tendencies**. This paper does not claim that the underlying training phenomena on which DSI depends are newly discovered. Prior work on distributional memorization, data-mixing ratios, procedural knowledge, and knowledge storage and extraction provides adjacent empirical foundations. The contribution here is to place these phenomena within KSC and provide a unified abstraction of the structural role of K. Building on DSI, this paper further proposes the **ES–DSI mechanism of transgenerational structural evolution**. AI systems and humans continually generate new candidate structures, while engineering environments, real-world feedback, and human society impose External Selection (ES) on those structures. Selection does not merely keep or discard an individual result. Through adoption, replication, dissemination, and long-term persistence, it reweights the relative distribution of structures in future data and knowledge environments. Only selected structures that enter data accessible to later AI systems, are actually included in training, and receive effective training exposure have the opportunity to be internalized through DSI into new parameterized K. Consequently, successful structures that were low-probability outcomes in the S of an earlier generation and required extensive search to discover may, when propagation and effective-training-exposure conditions are satisfied, become higher-probability initial candidates in later models. The cycle can be summarized as: **K_t → (S_t, C_t) → New Structures → ES → Reweighted Structural Distribution → Accessibility → Effective Training Exposure → DSI → K_{t+1} → (S_{t+1}, C_{t+1})** ES and DSI form the external and internal stages of this mechanism, respectively: **ES changes which structures are more prevalent in the world, while DSI changes which structures a model is more likely to generate when facing a problem.** Two transmission conditions connect the two: selected structures must enter data accessible to later AI systems and receive effective exposure in actual training. Software engineering is a candidate domain for observing ES–DSI because AI already participates in multiple forms of structural generation there, execution-based verification is relatively strong, human selection is frequent, and solution structures can be preserved in executable code. Model trainers can choose which data enter a training set and adjust the mixing ratios of different data sources, but control over data selection is not equivalent to control over structure creation. Real-world production and knowledge ecosystems can continually generate, validate, and accumulate new structural experience with transfer value. Expert curation, controlled experiments, high-quality synthetic data, and private engineering environments can also produce effective accumulation. For general-purpose models, distributed external selection in public digital environments may provide scale, diversity, and long-term real-world feedback that are difficult for any single trainer to reproduce independently, but its contribution relative to other sources of structural experience remains an empirical question requiring comparative tests. This paper further outlines observable, testable, and potentially quantifiable empirical paths, including the adoption and survival rates of candidate structures, propagation range, training exposure, changes in initial structural distributions, and task performance across model generations. It distinguishes between “the existence of differential retention” and “a positive contribution of such retention to capability accumulation.” ES does not itself guarantee that higher-quality structures will prevail. ES–DSI becomes a positive capability-accumulation mechanism only when real-world selection is sufficiently and consistently positively correlated with reusable structural quality, and when selected structures can enter environments accessible to later AI systems. This open ecological inheritance chain can carry both human-generated and AI-generated structures; only the component generated by AI or human–AI collaboration that produces an incremental effect in later AI systems constitutes the transgenerational AI self-feedback component examined in this paper. This paper does not claim that ES–DSI has been shown to be the dominant source of current AI capability growth. Instead, it proposes a testable long-term hypothesis: **as AI-generated structures account for a growing share of newly produced structures in the digital world, ES–DSI may become one of the important drivers of transgenerational AI capability growth.**