Agentic AI Model Adaptation for Time-Accuracy Tradeoffs
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Solution Overview
Problem
Conventional AI models face challenges such as overfitting, inefficiency, and inability to adapt to different domains, leading to suboptimal performance in various tasks, and are often optimized for syntactic and semantic correctness rather than functional correctness.
Innovation Solution
An agentic AI model framework that adapts to new domains by adjusting hyperparameters and selecting modules based on execution time and output accuracy, using natural language descriptions and metadata to minimize overfitting and optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If larger AI models are used to capture complex patterns, then output accuracy is improved, but execution time increases and efficiency deteriorates
Solution Approach 1:
The system dynamically selects between different AI model sizes (e.g., 7B, 13B, 70B parameters) based on the specific task requirements and constraints. This dynamic adaptation allows the system to use larger models when accuracy is critical and smaller models when execution time is constrained, resolving the contradiction between model size, accuracy, and speed.
Solution Approach 2:
The system changes key parameters including model size selection, context window size, temperature, and top-p values based on task characteristics. By adjusting these parameters dynamically, the system optimizes the balance between execution time and output accuracy for different inference scenarios.
2Stability of the object's composition
If frozen AI models with fixed weights are used, then model stability is maintained, but adaptability to specific situations deteriorates
Solution Approach 1:
The system segments the AI model into frozen base weights and adjustable hyperparameters. The base model weights remain fixed to maintain stability, while hyperparameters such as temperature, top-p, and context window size can be adjusted to adapt to specific situations, resolving the contradiction between stability and adaptability.
3Measurement precision
If supervised training with limited annotated data is used, then model fine-tuning is achieved, but overfitting risk increases
Solution Approach 1:
The system uses self-service mechanisms through automated hyperparameter optimization and dynamic configuration based on task characteristics. Instead of relying solely on supervised fine-tuning with limited data, the system adapts to new domains by adjusting hyperparameters and selecting appropriate model configurations, reducing overfitting risk while maintaining performance.
4Measurement precision
If AI models are optimized for syntactic and semantic correctness, then language quality is improved, but functional correctness deteriorates
Solution Approach 1:
The system changes the temperature parameter and other generation parameters based on the specific task requirements. For tasks requiring functional correctness, the system adjusts parameters to prioritize accurate information retrieval and reasoning, while for tasks requiring language quality, it optimizes for syntactic and semantic correctness, resolving the contradiction between the two objectives.
Data Source
AI summary
Systems and methods for adapting an agentic artificial intelligence (AI) model is provided. The systems and methods include extracting embeddings of a user input and determining an execution time and input domain according to the embeddings of the user input. The systems and methods further include developing an inference plan according to the execution time and input domain, and selecting modules that satisfy the inference plan considering output accuracy and execution time to satisfy an execution time accuracy tradeoff.