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

TL;DR

This paper introduces a cognitive simulation framework for text generation that incorporates human-like imperfections, improving model robustness and functional performance over traditional smooth data training methods.

Contribution

It proposes the Prompt-driven Cognitive Computing Framework (PMCSF) that models human cognitive processes to generate more realistic and effective synthetic data for language models.

Findings

Cognitive text closely matches human text with low divergence and high cognitive profile alignment.

Models trained with cognitive-perturbed data show significant risk reduction in financial stress tests.

The approach offers a promising solution to AI data-collapse by emphasizing cognitive realism over statistical smoothness.

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.