Quantum-Ready Multi-Model AI Truth Synthesis Engine with Bias and Poisoning Detection

Smart overview of the Invention

The invention introduces a sophisticated system for synthesizing high-confidence responses from multiple large language models (LLMs) using a quantum-compatible orchestration framework. It orchestrates inference from various AI models, automatically detecting divergence among their outputs, and synthesizes a unified response optimized for reliability and coherence through an arbitration engine. This process ensures the system can function effectively across classical and quantum environments, making it suitable for sensitive domains like law, healthcare, and cybersecurity.

A critical feature is its ability to detect and mitigate bias, prompt injection, and adversarial data poisoning. A specialized filter evaluates each model's output for indicators of sentiment skew, demographic bias, and adversarial patterns. These evaluations are based on semantic analysis, statistical profiling, and anomaly detection, creating a composite risk profile to guide the arbitration process. The system's feedback loop recalibrates trust scores and can trigger model fine-tuning, maintaining high inference integrity.

Trust weights are assigned to each LLM response, adjusting for reliability. Unreliable outputs are downweighted or excluded from synthesis by the arbitration engine. A feedback loop tracks errors and recalibrates model trust profiles, potentially triggering fine-tuning. This dynamic evaluation mechanism ensures that the system remains robust, particularly in high-stakes applications. The system supports both classical and quantum processing environments, allowing for flexible deployment.

The architecture is designed to be quantum-ready, operable in classical and quantum environments, and compatible with future quantum execution models. Quantum decision logic, modeled using variational quantum circuits, guides model routing or arbitration when high semantic divergence is detected. This ensures the system's adaptability to future quantum advancements without compromising current performance or interoperability.

The multi-threaded orchestration layer manages input prompting, model invocation, divergence detection, and output synthesis across heterogeneous LLMs. It supports routing decisions and consensus arbitration using probabilistic methods. The system's modular design allows for adaptability across various domains, making it particularly suited for secure communications, legal analysis, scientific modeling, and critical infrastructure management.