LLM-Augmented Agent Orchestration for Context Limits
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Solution Overview
Problem
Existing Large Language Model (LLM)-augmented Autonomous Agents (LAAs) are limited by the maximum size of context input, which restricts their ability to provide accurate next-action indications and complete complex tasks.
Innovation Solution
The proposed solution involves building an architecture of selected LAAs that jointly perform a target task by optimizing each LAA for a particular function through fine-tuning the underlying LLM or optimizing specific prompts. A controller determines which LAA is best suited for predicting the next action at every iteration, allowing for higher accuracy results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single LLM is used to process all context information, then the system structure remains simple, but the accuracy of next-action predictions deteriorates due to context input size limitations
Solution Approach 1:
The patent divides the single LLM system into multiple specialized LLMs, each responsible for processing specific aspects of the context information. This segmentation allows each LLM to operate within its optimal context window while collectively handling the full scope of information, thereby improving prediction accuracy without requiring a single oversized model.
Solution Approach 2:
The patent creates a multi-functional system where multiple LLMs work together to perform the overall task that would otherwise require a single universal LLM. Each LLM specializes in particular functions (e.g., code generation, natural language processing, data analysis), and their coordinated output achieves comprehensive task completion with higher precision.
2Reliability
If multiple LLMs are used to process different aspects of the task, then the accuracy of actions improves, but the device complexity increases
Solution Approach 1:
The patent implements a dynamic architecture where LLMs are selectively activated based on the specific task requirements and context. Rather than maintaining all LLMs in a static, always-active state, the system dynamically determines which LLMs to engage for each particular task, optimizing resource utilization while maintaining high reliability when needed.
Solution Approach 2:
The patent introduces an intermediary component that coordinates between multiple LLMs and the overall system. This intermediary manages the complexity by handling LLM selection, input routing, and output aggregation, allowing multiple specialized models to work together seamlessly without requiring complex direct interconnections between all components.
3Loss of information
If context information is distributed across multiple LLMs, then the limitation of maximum context input size is overcome, but the orchestration complexity increases
Solution Approach 1:
The patent employs a nested architecture where context information is hierarchically organized and distributed across multiple LLMs. Each LLM processes a nested subset of the overall context, with intermediate results being aggregated and passed to higher-level processing stages. This nesting allows comprehensive context utilization while managing orchestration complexity through hierarchical structure.
Solution Approach 2:
The patent ensures continuous information flow across the multi-LLM system through persistent context management. Rather than treating each LLM invocation as isolated, the system maintains continuous state and context across multiple LLM calls, allowing context information to be progressively built upon and refined through the sequence of LLM operations.
Data Source
AI summary
Embodiments described herein provide a method of predicting an action by a plurality of language model augmented agents (LAAs). In at least one embodiment, a controller receives a task instruction to be performed using an environment. The controller receives an observation of a first state from the environment. The controller selects a LAA from the plurality of LAAs based on the task instruction and the observation. The controller obtains an output from the selected LAA generated using an input combining the task instruction, the observation, and an LAA-specific prompt template. The controller determines the action based on the output. The controller causes the action to be performed on the environment thereby causing the first state of the environment to change to a second state.