Generative–Inverse Closure: A Unified Framework for Generation, Qualification and Recovery in Adaptive Dynamical Systems
- 1Independent Researcher
Abstract
Adaptive computational systems are often evaluated by forward generation, inverse diagnosis and recovery as separate tasks, allowing persistent but non-autonomous states, observer-compressed mechanisms or compensation-masked failures to be misclassified as valid effective states. We introduce Generative–Inverse Closure (GQR), a lineage-preserving framework in which generation, qualification and recovery form one conjunctive test. GQR combines explicit observation operators, non-compensatory admission axes, ablation–restoration–recovery evidence and typed closure debt. Archived computational studies expose distinct one-way failure modes. A new 64-seed single-lineage benchmark then generates relation-based coarse states, tests predictive and interventional qualification, applies controlled damage, restores the baseline dynamics and requires requalification after recovery. Generated candidates passed the declared qualification gate in 61/64 runs versus 4/64 randomized candidates, while 63/64 recovered candidates passed requalification. The result is methodological and model-level: GQR does not establish a universal physical law, but provides a falsifiable closure architecture for adaptive computational systems.
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BibTeX
@misc{wang2026generativeinverse,
title = {{Generative–Inverse Closure: A Unified Framework for Generation, Qualification and Recovery in Adaptive Dynamical Systems}},
author = {Yanghua Wang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23451677},
url = {https://zenodo.org/records/23451677},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Generative–Inverse Closure: A Unified Framework for Generation, Qualification and Recovery in Adaptive Dynamical Systems
AU - Yanghua Wang
PY - 2026
DA - 2026/10/18
PB - Zenodo
DO - 10.5281/zenodo.23451677
UR - https://zenodo.org/records/23451677
AB - Adaptive computational systems are often evaluated by forward generation, inverse diagnosis and recovery as separate tasks, allowing persistent but non-autonomous states, observer-compressed mechanisms or compensation-masked failures to be misclassified as valid effective states. We introduce Generative–Inverse Closure (GQR), a lineage-preserving framework in which generation, qualification and recovery form one conjunctive test. GQR combines explicit observation operators, non-compensatory admission axes, ablation–restoration–recovery evidence and typed closure debt. Archived computational studies expose distinct one-way failure modes. A new 64-seed single-lineage benchmark then generates relation-based coarse states, tests predictive and interventional qualification, applies controlled damage, restores the baseline dynamics and requires requalification after recovery. Generated candidates passed the declared qualification gate in 61/64 runs versus 4/64 randomized candidates, while 63/64 recovered candidates passed requalification. The result is methodological and model-level: GQR does not establish a universal physical law, but provides a falsifiable closure architecture for adaptive computational systems.
ER -