Hybrid LLM Reasoning Workflow for Speed-Accuracy Tradeoffs
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Solution Overview
Problem
Large language models (LLMs) face limitations in solving complex reasoning problems that require multiple steps of logical inference and tool usage, falling short of human expert performance.
Innovation Solution
A hybrid thinking framework that combines fast intuitive reasoning with slow deliberate analysis, dynamically switching between modes to tackle complex problems through direct chain-of-thought reasoning, decomposition into subtasks, and expert reasoning modules, leveraging both natural language and symbolic engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If LLMs use direct chain-of-thought reasoning for complex problems, then reasoning speed is improved, but accuracy deteriorates
Solution Approach 1:
The patent segments the reasoning process into multiple stages: fast thinking generates initial solutions quickly, while slow thinking provides deliberate verification and correction. This segmentation allows the system to maintain both speed (through fast initial generation) and accuracy (through slow verification), resolving the contradiction between reasoning speed and accuracy.
Solution Approach 2:
The fast thinking mode performs preliminary action by generating initial solutions and hypotheses quickly before the slow thinking mode verifies and refines them. This preliminary generation maintains speed while the subsequent verification ensures accuracy, addressing the speed-accuracy tradeoff.
2Measurement precision
If LLMs use slow deliberate analysis with decomposition into subtasks, then accuracy is improved, but productivity deteriorates
Solution Approach 1:
The patent implements a dynamic reasoning system that adapts between fast and slow thinking modes based on problem characteristics. For simple problems, the system uses fast thinking to maintain high productivity, while for complex problems requiring high accuracy, it transitions to slow deliberate analysis with task decomposition, thus balancing both accuracy and productivity.
Solution Approach 2:
The hybrid thinking system performs self-service by automatically selecting appropriate reasoning modes and dynamically allocating computational resources. The system self-regulates when to use fast versus slow thinking based on problem difficulty, eliminating the need for external control and optimizing the accuracy-productivity balance autonomously.
3Reliability
If LLMs apply iterative verification processes, then reliability is improved, but loss of time increases
Solution Approach 1:
The patent implements periodic verification where the slow thinking mode intermittently checks and validates solutions generated by fast thinking, rather than continuously verifying every step. This periodic action maintains reliability through regular verification while minimizing time loss by avoiding constant checking, allowing the system to balance reliability and verification time effectively.
Data Source
AI summary
A method performed by at least one processor, the method includes receiving a task query; inputting the task query, at a first stage of an automated hybrid task solving model, into one or more large language models (LLMs) to generate a first solution; determining, at the first stage of the automated hybrid task solving model, determining whether the first solution passes a first stage verification; and based on determining the first solution does not pass the first stage verification, iteratively applying a second stage of the automated hybrid task solving model to the task query until a final answer is verified.