Layered Memory Agent Alignment for Reliable Task Planning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional large language models (LLMs) face challenges in performing complex tasks due to unpredictable output, lack of self-awareness, misalignment with diverse human values, and inefficiencies in prompt engineering, leading to issues like AI hallucinations and resource-intensive fine-tuning, which are not scalable for dynamic environments.
Innovation Solution
Integrating a structured, layered memory system with adaptive machine learning processes and observer agents to align agents with user-specific roles and preferences, using Bayesian-inspired approaches and micro-prompts to enhance performance and reduce resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional LLMs are used to perform complex tasks, then they can handle diverse tasks with general knowledge, but they produce unpredictable output and suffer from AI hallucinations
Solution Approach 1:
The patent introduces an observer agent as an intermediary component that monitors and evaluates the output of the main LLM agent. This observer agent checks for hallucinations and validates responses before they are finalized, thereby improving output reliability without sacrificing the LLM's versatility in handling diverse tasks.
Solution Approach 2:
The system implements a feedback mechanism where the observer agent provides real-time evaluation and correction signals back to the main agent. This feedback loop allows the system to learn from mistakes and improve output consistency while maintaining the ability to handle various complex tasks.
2Reliability
If fine-tuning is applied to improve LLM alignment with user preferences, then task performance improves, but resource consumption increases significantly
Solution Approach 1:
Instead of performing full fine-tuning which consumes excessive resources, the patent applies partial adaptation through observer agents that learn user preferences incrementally. The observer agent captures and learns from user feedback in real-time, providing alignment improvements without the heavy computational cost of complete fine-tuning.
Solution Approach 2:
The system creates a lightweight copy or representation of user preferences through the observer agent's learned models, rather than modifying the main LLM's weights through resource-intensive fine-tuning. This copied preference model enables alignment with user preferences while consuming minimal computational resources.
3Measurement precision
If traditional prompt engineering is used to improve task performance, then accuracy improves, but it requires extensive manual effort and time
Solution Approach 1:
The observer agent automatically performs the function of prompt engineering by dynamically adjusting and optimizing prompts based on task requirements and user feedback. This self-service approach eliminates the need for manual prompt engineering while maintaining high task execution accuracy through automated prompt optimization.
Solution Approach 2:
The system performs preliminary analysis of task requirements and pre-configures optimal prompts before task execution. The observer agent learns from past interactions and prepares optimized prompts in advance, reducing the time needed for manual prompt engineering while maintaining high accuracy.
4Productivity
If LLMs operate autonomously without observation, then they can act quickly, but they lack self-awareness and may misalign with human values
Solution Approach 1:
The observer agent serves as an intermediary between the autonomous LLM and human users, providing real-time monitoring and evaluation. This intermediary layer ensures alignment with human values through continuous observation and feedback while maintaining the LLM's autonomous operation and response speed.
Data Source
AI summary
An example may use an objective to retrieve first context data from at least one first memory layer of a multi-layer memory associated with an automated agent, and cause the first context data to be presented via at least one first conversational dialog element. An example may determine context feedback data in response to the first context data, and cause the context feedback data to be stored in at least one second layer of the multi-layer memory. An example may use the objective, the first context data, the context feedback data, and at least one workflow to configure a first prompt. An example may use the configured prompt and a machine learning model to generate a plan including one or more tasks executable by at least the automated agent to complete the objective. An example may cause the plan to be presented via at least one second conversational dialog element.