Persisted Agent State Serialization for Deterministic LLM Context
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Solution Overview
Problem
Existing systems using large language models store information in unstructured formats, leading to inaccuracies, inefficiencies, and ambiguities due to non-deterministic responses, excessive computational loads, and unneeded resource utilization.
Innovation Solution
The structured data management system generates and stores persisted data elements with a fixed data structure, utilizing a context engine and interpreter to serialize the state of the large language model, allowing for accurate, efficient, and flexible response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If unstructured data is used to store session context, then the system can maintain conversation history, but the responses become non-deterministic and inaccurate
Solution Approach 1:
The patent changes the data structure from unstructured natural language to structured parameters with defined schemas. The context is represented as a collection of named parameters (e.g., user_name, session_id, turn_number) with explicit data types and formats, transforming the information representation to achieve deterministic and accurate responses while maintaining complete contextual information.
2Productivity
If unstructured data is stored for session context, then conversation history can be maintained, but storage requirements and processing power increase excessively
Solution Approach 1:
The patent transforms unstructured text data into structured parameter representations, significantly reducing the amount of data that needs to be stored and processed. By using named parameters with specific data types instead of raw text, the system maintains full contextual information while reducing storage requirements and processing overhead.
Solution Approach 2:
The patent segments the conversation context into discrete, named parameters (user_name, session_id, turn_number, etc.), allowing the system to store and process only the essential information from each turn. This segmentation enables efficient retrieval and processing of specific context elements without needing to reprocess entire conversation histories.
3Productivity
If unstructured data is used for context, then the system can store conversation history, but additional computing resources are required for search, pre-processing, and ambiguity resolution
Solution Approach 1:
The patent eliminates the need for complex search, pre-processing, and ambiguity resolution operations by representing context as structured parameters with explicit definitions. The system directly queries and processes parameter values without requiring additional computational tools or complex processing pipelines.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating, storing, and accessing persisted data elements in a large language model framework. For instance, the disclosed systems can generate a code segment that includes a set of parameters and a function with a large language model associated with a context engine. In some cases, the disclosed systems can determine the serialized state of the interpreter based on executing the code segment. In one or more implementations, the disclosed systems can generate a persisted data element from the serialized state of the interpreter and store the persisted data element in an interpreter data store. The disclosed systems can further access the persisted data element from the data store to generate responses or perform tasks in the same or subsequent sessions.