Tabular Policy Action Models to Prevent Clinical AI Hallucinations
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Solution Overview
Problem
Existing action models for computer control, particularly in clinical applications, face challenges such as data inefficiency, lack of interpretability, hallucinations, and model regression, making them unsuitable for reliable and efficient task execution.
Innovation Solution
Implementing a tabular policy action model that uses a table to map states to actions, avoiding the need for large datasets and neural network training, ensuring interpretability, preventing hallucinations, and eliminating model regression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural network-based action models are used for computer control, then the model can learn complex patterns from data, but the model requires large datasets and extensive training resources
Solution Approach 1:
The patent segments the action model into distinct components: a policy module that stores discrete state-action pairs in tabular form, and a separate value module. This segmentation allows the policy to be data-efficient while the value module handles complexity, resolving the contradiction between adaptability and data requirements.
Solution Approach 2:
The patent replaces the neural network's continuous parameter-based decision mechanism with a discrete table lookup mechanism. Instead of using heavy neural network machinery to determine actions, the system uses simple index-based retrieval from pre-computed tables, dramatically reducing data and computational requirements while maintaining decision capability.
2Adaptability or versatility
If neural network-based action models are used, then the model can handle complex tasks, but the model lacks interpretability
Solution Approach 1:
By separating the policy into discrete, human-readable state-action pairs stored in tables, the patent enables interpretability of the decision-making process. Each entry in the policy table can be explicitly inspected and understood, unlike the opaque weights and biases in neural networks, while still handling complex tasks through comprehensive state coverage.
3Adaptability or versatility
If neural network-based action models are used, then the model can generalize from data, but the model is prone to hallucinations and model regression
Solution Approach 1:
The patent performs preliminary computation to build comprehensive policy tables that cover all possible states beforehand. By pre-computing and storing the correct actions for every conceivable state in tabular form, the system eliminates runtime hallucinations and ensures consistent, reliable predictions without needing to generalize from limited training data.
4Quantity of substance
If tabular policy action models are used, then the model is more data-efficient and interpretable, but the model may have limited adaptability for unseen states
Solution Approach 1:
The patent creates a universal policy table that can handle any state by using hash-based indexing and pattern matching. The tabular structure is designed to be extensible and adaptable to unseen states through systematic state representation and lookup mechanisms, allowing the simple tabular approach to maintain both data efficiency and broad adaptability.
Data Source
AI summary
In some aspects, the present disclosure provides a computer-implemented system comprising: a digital processing device comprising: at least one processor, an operating system configured to perform executable instructions, a memory, and a computer program including instructions executable by the digital processing device to perform a computer task by a user expressing to the computer-implemented system, and wherein the computer-implemented system is configured to perform the task by generating and executing a computer-executable program.