Creating a reading inference program involves several steps. Here’s a simplified outline of how you can approach building such a program:

1. Define the problem: Clearly define the goal of your reading inference program. What specific tasks do you want it to perform? For example, do you want it to extract key information from a given text, make predictions, or answer questions based on the text?

2. Gather training data: Collect a dataset of texts and their corresponding inferences or answers. This dataset will be used to train your program to make accurate inferences.

3. Preprocess the data: Clean and preprocess the text data to remove any unnecessary characters, punctuation, or formatting. You may also need to tokenize the text into individual words or phrases.

4. Feature extraction: Extract relevant features from the text that can help your program make inferences. This could include word frequencies, sentence structure, or semantic relationships between words.

5. Train a model: Choose a suitable machine learning algorithm, such as a neural network or a decision tree, and train it using your preprocessed data. The model should learn to make accurate inferences based on the provided examples.

6. Evaluate the model: Test the trained model on a separate set of data to evaluate its performance. Measure metrics such as accuracy, precision, recall, or F1 score to assess how well the program is making inferences.

7. Fine-tune and iterate: Analyze the performance of your program and make necessary adjustments to improve its accuracy. This may involve tweaking the model architecture, adjusting hyperparameters, or collecting more training data.

8. Deploy the program: Once you are satisfied with the performance of your reading inference program, deploy it as a standalone application or integrate it into an existing system.

Remember that building a robust reading inference program can be a complex task, and it may require advanced natural language processing techniques and machine learning algorithms.