Partial Quantum Mirroring for Real-Time AI Hallucination Detection
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Solution Overview
Problem
Conventional systems fail to detect and mitigate AI hallucinations and excessive branching in generative AI models in real time, despite the enhanced capabilities of quantum computing systems.
Innovation Solution
A quantum computing system is used to partially mirror AI search results, allowing for real-time detection of AI hallucinations and branching, with continuous hashing to identify mismatches, and remediation through deletion or redirection of incorrect outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems are used to limit AI models and restrict input data sets to minimize hallucinations, then AI hallucination risk is reduced, but real-time detection and mitigation capability is lost
Solution Approach 1:
The system performs preliminary actions by continuously hashing and comparing data segments before complete AI processing occurs. The quantum computing system pre-computes hash values of incoming data segments and maintains a rolling hash comparison, enabling real-time detection of AI hallucinations before they propagate through the full system.
Solution Approach 2:
The patent introduces hashing algorithms as an intermediary mechanism between the AI model and the output verification process. By using hash values as intermediaries to represent data segments, the system enables efficient real-time comparison without requiring full data transmission or processing, thus maintaining reliability while enabling real-time detection.
2Measurement precision
If quantum computing systems are used to fully mirror AI search results for real-time monitoring, then detection accuracy is improved, but computational resources and complexity increase significantly
Solution Approach 1:
The system segments the AI search results into discrete data segments that are processed individually through hashing. Instead of mirroring entire AI search result sets, the quantum computing system divides the data stream into manageable segments, computes hash values for each segment, and performs comparisons incrementally, reducing overall system complexity while maintaining detection accuracy.
Solution Approach 2:
The patent changes the parameter representation from full data segments to hash values. By transforming the data into hash parameter space, the system achieves efficient comparison with reduced computational complexity. The hash function maps complex data segments to simplified numerical representations that can be quickly compared by the quantum computing system.
3Reliability
If continuous hashing is performed on all AI data segments for real-time verification, then detection reliability is improved, but processing speed and efficiency decrease
Solution Approach 1:
The system applies partial action by performing hashing and comparison operations on selected data segments rather than all segments. The rolling hash mechanism computes comparisons for critical segments while skipping or reducing verification on less critical portions, maintaining sufficient reliability while preserving overall processing speed and productivity.
Data Source
AI summary
Systems, methods, and apparatus are provided for remediating an AI hallucination and determining and limiting excessive branching. An AI query may be received at multiple processors including a quantum processor or at a quantum processor having multiple threads, and an AI search may be executed at multiple processors or on multiple quantum threads. A continuous hashing algorithm may hash the AI search data and partially mirrored AI search data and compare the hashes. When the hashes are not identical, the partially mirrored AI search data may be deleted. The AI search may be terminated and reinitiated at the last point the hashes are identical. The AI search data may be partially mirrored at the point that the search is resumed. The results of partial mirroring may be fed back to update the AI model.