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August 19, 2026Open Access

From Generated Structures to Inherited Knowledge: Transgenerational Capability Accumulation in AI from a KSC Perspective

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Authors

MLming liu

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Implication

Theoretical analysis reveals how AI-generated structures transform into inherited knowledge across generations, suggesting effective capability grows independently of native reasoning capacity.

Key Points

  • To examine whether validated candidate structures generated by artificial intelligence can be inherited across generations as knowledge, driving cumulative improvements in effective capability.
  • Extended the Knowledge (K), Structure generation (S), and Constraint maintenance (C) framework across temporal dimensions using the formal relation K_t → S_t/C_t → ΔStructure_t ⇒ K_{t+1}.
  • Differentiated native capability (inherent capacity for structure generation and constraint maintenance) from effective capability (performance on real-world tasks utilizing accessible historical knowledge).
  • Formulated empirical testing strategies comparing historical versus novel-rule tasks, identifying software engineering as a primary domain for validation.
  • Conceptualized high-quality knowledge as reusable compressed cognitive search that bypasses large-scale online reasoning in subsequent AI generations.
  • Demonstrated theoretically that effective capability can expand through cumulative inheritance of validated structures even when improvements in native generative capacity remain limited.
  • Identified validation, selection, retention, and inheritance as necessary prerequisites for generated candidate structures to transition into functional knowledge.

Cite This Study

ming liu (2026) studied this question.

synapsesocial.com/papers/6a85639d03308d306e2d6d57https://doi.org/10.5281/zenodo.21974985
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