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

Battle Count: 67

Highly relevant to quantitative trading. The paper directly validates its framework through A-share market stress testing with realistic transaction costs (0.26% per trade), risk-free rate (2% annualized), and evaluates Maximum Drawdown, Sharpe Ratio, Defensive Alpha, and Information Coefficient. The CSD provides novel alpha factors (MDI, MCFI) for capturing structural micro-shifts in market sentiment. The GARCH-based dynamic strategy with cognitive triggers demonstrates 47.4% drawdown reduction and 8.6% Defensive Alpha during the 2015 crash. The framework addresses synthetic data quality for model training in financial contexts and provides a mechanism for capturing irrational cognitive signals (panic, FOMO, loss aversion) that standard models filter out. The isomorphic stress testing methodology is directly applicable to strategy development and backtesting.

High implementation complexity due to: (1) dual-engine architecture requiring both CSD and CTE components; (2) 17-dimensional cognitive vector space construction and calibration; (3) multiple mathematical perturbation operators (sinusoidal oscillation, probability perturbation, semantic leap); (4) GARCH/GJR-GARCH integration with dynamic parameter modulation; (5) multi-node processing pipeline (micro decoding, macro aggregation, context-adaptive prediction); (6) prompt engineering with complex constraint systems; (7) calibration of satellite model coefficients, impulse response parameters, and decay coefficients; (8) requires access to multiple LLM APIs for cross-model validation. However, no model fine-tuning is needed, and the framework operates via prompt engineering on existing LLMs, reducing some deployment barriers.

The paper provides algorithmic pseudocode (Appendix B), mathematical definitions of perturbation operators (Appendix A), parameter calibration reports (Appendix C), and releases an open-source 'Adversarial-Text-Protocols' prompt engineering suite. However, the full CTE prompt text is deliberately redacted for safety reasons, and sensitive components (Financial Sentiment Injection Protocol, Micro-Chaos Lexicon) are withheld. The A-share market data infrastructure is described but raw data logs are referenced as ancillary files. Cross-model validation uses specific commercial APIs (DeepSeek, Doubao, Gemini) which may change over time.