Self-Improving Model for Agentic Visual Program Synthesis
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Solution Overview
Problem
Training AI models for agentic visual program synthesis is time and resource intensive, and existing methods face challenges such as privacy risks, limited domain-specific language knowledge, and scalability issues due to reliance on external APIs and large datasets.
Innovation Solution
A self-improving model is trained using a continuous learning approach that decomposes input questions into vision model tasks, corrects task outputs based on feedback, generates optimal training tuples, and refines itself iteratively, allowing it to adapt and improve with new data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI models are trained using traditional methods with large datasets and external APIs, then accuracy in tasks like object detection and scene understanding can be achieved, but training time and computational resources increase significantly
Solution Approach 1:
The system employs self-supervised learning where the AI model generates its own training data by executing visual programs on synthetic images. The model creates task descriptions, generates corresponding visual programs, executes them to produce task outputs, and uses these self-generated outputs as training labels. This eliminates dependency on large external datasets and manual annotations, enabling continuous self-improvement without significant additional training time or computational resources
Solution Approach 2:
The system pre-generates synthetic training data by creating visual programs and their corresponding task outputs before actual training occurs. By preparing training tuples (image, task description, visual program, task output) in advance through automated generation and execution, the model has ready-to-use training material that eliminates the need for time-consuming traditional training processes
2Measurement precision
If traditional training methods using large datasets are used, then model accuracy improves, but scalability and adaptability to new domains deteriorate
Solution Approach 1:
The AI model continuously generates new training data by creating visual programs for new task descriptions and domains. When exposed to new images or task types, the system automatically generates corresponding visual programs and task outputs, creating new training tuples on-the-fly. This enables the model to adapt to new domains and scale to different applications without requiring retraining on large external datasets
Solution Approach 2:
The training data generation process is dynamic and adaptive rather than static. The system continuously creates new visual programs and task outputs based on current model capabilities and newly encountered data. This dynamic generation allows the training dataset to evolve and expand automatically, improving scalability and adaptability to emerging domains and task types
3Measurement precision
If external APIs and large datasets are used for training, then initial model performance can be achieved, but privacy risks and dependency on external resources increase
Solution Approach 1:
The system eliminates dependency on external APIs and large external datasets by generating all training data internally. The AI model creates visual programs, executes them on synthetic images, and generates task outputs that serve as training labels. This self-contained approach removes privacy risks associated with external data sources and eliminates the need for external API dependencies, while maintaining model performance through continuous self-generated training
Data Source
AI summary
Systems and methods for a self-improving model for agentic visual program synthesis. An agent can be continuously trained using an optimal training tuple to perform a corrective action to a monitored entity which in turn generates new input data for the training. To train the agent, an input question can be decomposed into vision model tasks to generate task outputs. The task outputs can be corrected based on feedback to obtain corrected task outputs. The optimal training tuple can be generated by comparing an optimal tuple threshold with a similarity score of the input image, the input question, and the corrected task outputs.