We previously examined event !flows and the physics of their occurrence.

Another paradigm that allows us to view event !flows through the lens of language model operations:

Let us imagine our !reality as a large analog LLM (aLLM). One might assume that any event could occur within it, but that is not the case. For example, if you want to go to the store, you need to execute a strict chain of actions (aLLM transactions):

Get dressed → take wallet → get into car → drive to store → park → grab a cart, etc.

• Chain failure: If any token or transaction is missing, the entire generation chain will break. If you forgot your car keys, a model rollback will occur at the "drive to store" transaction.

• Search for alternatives: The analog LLM will begin searching for other options to complete the transaction using alternative tokens: take a bus, walk, etc.

Generation chains of an analog LLM are these very same event !flows. If we want to go to the store, there is no need to compute the entire potential cloud of tokens and transactions. Each subsequent token can only be followed by a limited set of options; therefore, it is sufficient to compute the most probable path for the event !flow (the aLLM generation chain) to run down.

This principle is utilized by the aLLM bot, which models which event token will prove most probable in the generation chain being processed.