Generative Model Agent for Verified Multi-Step Data Analysis
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Solution Overview
Problem
Generative models such as large language models (LLMs) and vision language models (VLMs) struggle with logical tasks like answering mathematical questions or performing analytic tasks on data.
Innovation Solution
Utilizing generative models to predict executable state machines, such as source code, for multi-turn dialogs that allow analysis and refinement of user-uploaded files, including generation of interactive visualizations, by assembling prompts with external data sources and iteratively refining the analysis through execution and evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative models are used for predictive tasks, then creativity and pattern recognition are improved, but logical reasoning and analytic task performance deteriorate
Solution Approach 1:
The system segments the task into two distinct components: a generative model for creating hypotheses/patterns and a separate evaluation mechanism for logical verification. The generative model generates multiple possible interpretations of data patterns, while a deterministic evaluation component systematically verifies each hypothesis against the actual data, ensuring logical accuracy while maintaining creative pattern recognition.
Solution Approach 2:
An intermediary evaluation layer is introduced between the generative model and the final output. This intermediary component acts as a mediator that takes the creative outputs from the generative model, systematically evaluates them against logical constraints and data facts, and only passes verified results forward, thus bridging the gap between creative flexibility and logical rigor.
2Productivity
If generative models generate code directly, then development speed is improved, but code accuracy and logical correctness deteriorate
Solution Approach 1:
The system performs preliminary generation of multiple code variants quickly using the generative model, then systematically evaluates each variant before final selection. This preliminary action allows rapid exploration of multiple solutions without committing to potentially incorrect code, maintaining both speed and accuracy by verifying correctness before deployment.
Solution Approach 2:
A feedback loop is implemented where generated code is automatically evaluated against test cases and data constraints. The evaluation results feed back into the generation process, allowing the system to learn from errors and improve subsequent code generation. This continuous feedback ensures code correctness while maintaining high productivity through automated verification.
3Measurement precision
If complex multi-step analysis is performed, then analytical depth is improved, but processing time and computational resources deteriorate
Solution Approach 1:
The system performs partial analysis by generating multiple hypotheses at different levels of detail and evaluating them in sequence. Rather than completing one exhaustive analysis, it generates several partial analyses with varying depths, evaluates their promise, and focuses computational resources on the most promising paths, achieving deep analysis where needed while avoiding unnecessary computational expenditure.
Solution Approach 2:
The analysis process is made dynamic and adaptive, adjusting the depth and scope of analysis based on intermediate results. The system starts with broader, faster analyses and dynamically deepens the investigation only in areas where initial results indicate potential value, optimizing the balance between analysis depth and processing time through adaptive resource allocation.
Data Source
AI summary
Implementations are provided for facilitating multi-turn dialogs with a generative model-based agent (GMAgent) that allow for multi-step analysis of external data source(s), including refinement of that analysis. In various implementations, data indicative of a first query and external data source(s) may be assembled into a first prompt. The first prompt may be processed using generative model(s) to generate first output data that includes first source code that is executable to perform an analytic task on data from the external data source(s). The first source code may be executed to perform the analytic task using the external data source(s) and generate analytic output. The analytic output may be assembled into a second prompt with a command to determine whether the analytic output satisfies the first query. The second prompt may be processed using generative model(s) to generate second output data that indicates whether the analytic output satisfies the first query.