The Necessity of Imperfection:Reversing Model Collapse via Simulating Cognitive Boundedness

• URL: http://arxiv.org/abs/2512.01354v3
• Date: Mon, 08 Dec 2025 22:57:17 GMT
• Title: The Necessity of Imperfection:Reversing Model Collapse via Simulating Cognitive Boundedness
• Authors: Zhongjie Jiang,
• Abstract summary: This paper proposes a paradigm shift: instead of imitating the surface properties of data, we simulate the cognitive processes that generate human text.<n>We introduce the Prompt-driven Cognitive Computing Framework (PMCSF) that reverse-engineers unstructured text into structured cognitive vectors.<n>Our findings demonstrate that modelling human cognitive limitations -- not copying surface data -- enables synthetic data with genuine functional gain.
• Score: 0.284279467589473
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although synthetic data is widely promoted as a remedy, its prevailing production paradigm -- one optimizing for statistical smoothness -- systematically removes the long-tail, cognitively grounded irregularities that characterize human text. Prolonged training on such statistically optimal but cognitively impoverished data accelerates model collapse. This paper proposes a paradigm shift: instead of imitating the surface properties of data, we simulate the cognitive processes that generate human text. We introduce the Prompt-driven Cognitive Computing Framework (PMCSF), whose core consists of a Cognitive State Decoder (CSD) that reverse-engineers unstructured text into structured cognitive vectors, and a Cognitive Text Encoder (CTE) that re-materializes these states into text enriched with human-typical imperfections via mathematically defined Cognitive Perturbation Operators. The framework is validated through a two-stage objective evaluation pipeline. First, in cognitive codec verification, CTE text yields a Jensen-Shannon divergence of 0.0614 from human text (vs. 0.4431 for standard LLM output), passes double-blind professional media review, and achieves an intraclass correlation coefficient ICC > 0.9 for cognitive profile alignment across heterogeneous models. Second, in functional gain evaluation, isomorphic stress tests in the A-share market show that strategies incorporating CTE-generated data reduce maximum drawdown by 47.4% during the 2015 crash and deliver 8.6% Defensive Alpha, exceeding transaction costs by a factor of 33. Our findings demonstrate that modelling human cognitive limitations -- not copying surface data -- enables synthetic data with genuine functional gain, offering a viable technical pathway toward resolving the AI data-collapse crisis.

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