Eliminating Polarity Hallucinations and Order Book Horizon Truncation Bias in Autonomous Multi-Agent Quantitative Systems: Schmitt-Trigger Factor Ontologies, Scroll-Out Resistant Deep OFI, and Slippage-Proof Pyramiding

Autonomous financial trading architectures driven by Large Language Models (LLMs) and high-frequency Limit Order Book (LOB) telemetry suffer from three structural failure modes that degrade out-of-sample Sharpe ratios and induce tail-risk drawdowns in live execution: (1) Semantic Polarity Inversion, wherein generative agents misinterpret inverse valuation or macro-stress indicators (e.g., EV/EBITDA, credit spreads) as positively correlated with forward returns, combined with Boundary Chattering when factor z-scores oscillate across decision thresholds; (2) Order Book Horizon Truncation Bias (The Scroll-Out Spoofing Illusion), a microstructure distortion in finite K-level moving-window order book feeds where rapid upward price appreciation causes resting bid liquidity to drop below the K-th visible depth horizon, falsely mimicking malicious institutional spoofing cancellations; and (3) Pyramiding Ruin Under Jump Slippage, where adding exposure to winning positions elevates the aggregate break-even price to a level vulnerable to gap-down stop-loss slippage. In this paper, we formulate a unified mathematical framework to eliminate all three vulnerabilities: Deterministic Factor Ontology Contracts with a Discrete-Time Schmitt-Trigger Hysteresis Operator, a Scroll-Out Resistant Deep Order Flow Imbalance (OFI) Estimator with a visible-window boundary invariant and 0.12s twin-snapshot differentials, and the closed-form Slippage-Proof Pyramiding Boundary Inequality. Validated across 1,000 property-based formal verification trajectories (Hypothesis/oss-fuzz) with zero invariant violations and 5-year empirical evidence (2021-2026)