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

ArXiv ID: 2512.01354 “View on arXiv”

Authors: Zhongjie Jiang

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.

Keywords: synthetic data, cognitive computing, model collapse, defensive alpha, A-share market

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts including a 17-dimensional vector space, Jensen–Shannon divergence, intraclass correlation coefficients, and a ‘Cognitive Perturbation Operators’ framework. It demonstrates high empirical rigor through specific quantitative results (e.g., 47.4% drawdown reduction, 8.6% Defensive Alpha) in a real-world market stress test (A-share market 2015 crash) with validated data pipelines.

  flowchart TD
    A["Research Goal:<br>Reverse Model Collapse via<br>Simulating Cognitive Boundedness"] --> B["Methodology:<br>Prompt-driven Cognitive Computing Framework PMCSF"]

    subgraph B_Method ["PMCSF Core Mechanisms"]
        B1["Cognitive State Decoder CSD<br>Reverse-engineers text → cognitive vectors"] --> B2["Cognitive Perturbation Operators<br>Mathematically defines human imperfections"]
        B2 --> B3["Cognitive Text Encoder CTE<br>Re-materializes states → enriched text"]
    end

    B --> C["Input Data:<br>Unstructured Human Text"]

    subgraph D_Process ["Two-Stage Evaluation Pipeline"]
        direction LR
        D1["Stage 1: Cognitive Codec Verification<br>Jensen-Shannon Divergence: 0.0614<br>ICC > 0.9"] --> D2["Stage 2: Functional Gain Evaluation<br>A-share Market Stress Test"]
    end

    B3 --> D_Process

    E["Key Findings & Outcomes"] --> F["Technical Pathway:<br>Modeling limitations enables<br>functional synthetic data"]
    E --> G["Performance Gains:<br>- 47.4% Max Drawdown Reduction<br>- 8.6% Defensive Alpha<br>- Exceeds transaction costs 33x"]

    D_Process --> E