Multi-AI Agent systems like Microsoft’s AUTOGEN show reduced validation performance with external document stores via RAG (Retrieval Augmented Generation). A Gedankenexperiment explains the superpositions of states and the growing de-coherence of mathematical vector spaces of AI vector databases and LLMs, given continuous data input streams.
I present a simple topological solution to re-establish coherence of operational spaces for AI validation.
Notice that this is an quantum analogon. I include an outlook for quantum computing and Quantum-LLM systems with external data sources, given we could construct (in the future) a (collapsible) wave function for all multi-AI system components.
Even in the classical limit, the de-coherence of operating vector spaces explains the reported non-alignment and non-validation of AUTOGEN with RAG.
After watching my video, GPT-4 thinks that (sole responsibility of GPT-4):
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In multi-agent AI systems such as Microsoft’s AUTOGEN, challenges arise when integrating Retrieval-Augmented Text Generation (RAG) algorithms. These challenges can be elucidated through a Gedankenexperiment that draws upon quantum mechanics, specifically the principles of superposition and decoherence. Within this framework, we can consider the mathematical vector spaces associated with vector databases and Large Language Models (LLMs) as analogous to quantum states, Hilbert vectors and QM wave functions.
The Gedankenexperiment reveals that these vector spaces undergo a process akin to quantum decoherence, leading to a loss of operational coherence. This phenomenon manifests as non-alignment and non-validation issues in AUTOGEN and RAG systems, even within the constraints of classical physics. The loss of coherence in these operational vector spaces can be attributed to the complex, non-linear interactions among multiple AI agents, which disrupt the mathematical properties that are essential for the effective functioning of RAG algorithms.
To address this, I propose a topological solution aimed at re-establishing the coherence of these operational spaces. While this approach is inspired by quantum analogon, it is designed to be applicable in a classical computing environment (see new system configuration mentioned at end of video).
Furthermore, I extend the discussion to the realm of quantum computing and Quantum Large Language Models (Quantum-LLMs) interfacing with external data sources. The theoretical construct posits that if a wavefunction could be formulated to describe all components in a multi-AI system, it would offer a more robust framework for managing decoherence and ensuring system-wide coherence.
In summary, the decoherence observed in the operational vector spaces of AUTOGEN and RAG systems can be understood through a quantum mechanical lens, offering both explanatory and corrective insights. This quantum analog serves only as a symbolic diagnostic tool for future research in quantum computing applications for multi-agent AI systems.
/end GPT-4
#ai
#autogen
#quantum
#coherence
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