LLM Logic Feedback Architecture for Deterministic Symbolic Reasoning
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Solution Overview
Problem
Large language models (LLMs) face challenges with non-deterministic and inconsistent outputs, making them unsuitable for mission-critical applications, while combinational and sequential logic circuits provide reliability but lack adaptability.
Innovation Solution
A hybrid AI system integrating a language model with combinational and sequential logic, utilizing a feedback loop to dynamically optimize symbolic logic expressions and event sequences, ensuring deterministic and adaptive performance through a symbolic processing unit, combinational logic, sequential logic state machine, and evolution code generator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If large language models are used for natural language processing, then adaptability and contextual reasoning are improved, but output reliability and determinism deteriorate
Solution Approach 1:
The patent implements a feedback loop where the logic processing unit evaluates symbolic logic expressions generated by the LLM and sends corrections back to the language model. This closed-loop feedback mechanism allows the system to maintain the adaptability of LLMs while improving output reliability through deterministic logic validation and iterative correction.
2Reliability
If combinational and sequential logic circuits are used for processing, then determinism and reliability are improved, but adaptability and flexibility deteriorate
Solution Approach 1:
The patent introduces a symbolic processing unit as an intermediary between the LLM and logic circuits. This intermediary translates natural language into symbolic logic expressions that can be evaluated by deterministic logic circuits, while the feedback loop allows the system to adapt to environmental changes by learning from logic evaluation results.
3Reliability
If logic circuits are made static for reliability, then deterministic operation is maintained, but responsiveness to environmental change deteriorates
Solution Approach 1:
The patent creates a dynamic system where the logic processing unit is not completely static but can be reconfigured through feedback from environmental evaluation. The system maintains deterministic operation during execution while adapting its logic expressions over time based on feedback from the language model and environmental changes, achieving both reliability and responsiveness.
Data Source
AI summary
An artificial intelligence system may include a language center, a logic processing unit, and an evolution code generator. In one embodiment, the logic processing unit may include a symbolic processing unit, a combinational logic, and a sequential logic state machine. A natural language query can be transmitted to the symbolic processing unit from the language center to produce symbolic logic expressions, and then transmitted to the combinational logic and the sequential logic state machine to produce optimized symbolic outputs and validated state transitions, which can be sent to the evolution code generator for further optimization. This further optimized language can be transmitted back to the combinational logic to correct logic contradictory, and/or to the sequential logic state machine to trigger for a state transition in the event sequence.