This work is intended as a blueprint for building a particular class of observer-state world models using standard ML components and design patterns. It aims to model how an observer's latent predictive state (experience/qualia) evolves before, during, and after a proposition, then encode that process into trainable operational objects.

The meta narrative of this paper suggests four things:

1. I started with a grand theory and pulled back toward the middle of the paper to give maximum engineering clarity, robustness, and verifiability in the portion that can actually be defended, while still not divulging all of the proprietary work underneath it. There are feature families in people that appear extremely predictive and relatively stable. I do not want to expose too much of the deeper ontological categorical layer because I think the human mind behaves much like a market: alpha only persists while it is not universally known, flattened, and exploited. The hypothesis you should infer from the writing is this: these task-relevant predictors can be used as footholds, then generalized beyond their initial scope, producing new categorical ontologies and, eventually, a true foundation model for experience built from the methods specified in this work.
2. The results and methodologies you are reading follow a narrative thread, but the final product was not derived linearly. It came out of an experimental elimination process that discarded many possible avenues, either because they depended too heavily on non-latent-space-first approaches or because they collapsed into hyperspecialized solutions to narrower tasks.
3. The point of the paper is not really to create task-relevant prediction models in the ordinary sense. Those should be understood as a byproduct of constructing a predictive latent object over the observer and the world.
4. Time is the single hardest problem being solved for throughout this work. If a method looks obtuse or unnecessarily complex, even when a simpler implementation could appear to do the same local job, you should consider that I am trying to extract latent objects that remain stable across time and regime rather than merely fit a surface-level task. Anyone can build a performant neural network to increase click-through rate or sell a piece of software more efficiently. That is not the end goal here. The end goal is to understand the nature of observers and propositions in complex environments; predictability is a byproduct of gaining access to the ontology this work is trying to construct.

Lastly, much of the philosophy is explicative of the ML machinery and was essential to my own process of discovering this framework. It is not essential to the thesis that you fully accept or even believe all of it.