In this series, Being is the system's state now. Becoming is how that state changes. We have used models to describe both. Before trusting a model's explanation or prediction about a real system, we need to ask what has actually been established. A correct calculation alone does not show that the model fits the world. Use this final episode to separate assumptions, derived results and empirical evidence. Keep asking: which claims still need a test?

We have used several kinds of statements. We specified small models, derived consequences, checked calculations and examined a reported finite-model comparison. A proposed application to a person or another machine adds a correspondence claim. Evidence that supports one of these statements does not automatically establish all the others.

In M0, u and v have different after-PULSE outputs. For w and z, either no RESET occurs or the first RESET brings both cases to u. That argument supports agreement for every finite sequence of the supplied buttons. Local partition and path checks corroborated the calculation. Testing a finite selection of paths alone would not justify every future sequence, and none of these checks observed a biological mind.

The noisy-reader model M1’s updated probabilities followed from a supplied prior, sensor likelihoods and independence assumptions. We did not measure that reader in a laboratory. R4 used inherited finite-benchmark evidence; its film reports model-average failures and limited policy comparisons. These examples can explain mechanisms and bounded results. They do not by themselves validate a general theory of cognition or show that a self-model label improves an architecture.

Suppose we propose that a representation predicts which differences a particular observer can use. First specify what the states and observations represent in that system. Declare the task, available information and measured outcome. Compare a relevant alternative under matched access and any resource constraints the claim requires. Keep some cases out of model development, and state what result would count against the claim. This is a proposal for evaluation, not a study conducted by this playlist.

A valid derivation, a correct implementation and a useful prediction answer different questions from what is new and what review a claim has received. A local AI check is not external peer review. A source page or an attractive video is not an independent replication. Record the actual provenance and review status rather than inferring them from presentation quality.

Classify this statement: our program reproduced the stated posterior calculation. Does it establish the sensor’s accuracy in a real robot?

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No. It supports that calculation under the supplied model. The reader’s real accuracy would require relevant measurements. Likewise, successful task prediction does not by itself establish subjective experience; that is a further claim, not another name for the prediction score.

Being and Becoming now gives us a set of precise questions. What is the state, and how is it described? What can be observed, and which distinctions matter for a task? Which experiments can obtain them? How do actions change the state, and how does a model keep up? We have worked bounded examples and marked the next empirical questions. Use the optional branches when a specific problem calls for their mathematics, and keep each further claim as explicit as the examples here.