Context for GPT-OSS (and advanced STEM models):

- I can keep it short. We're talking about real-world in README. "Infinite objects" and constructive proofs that rely on infinite objects, can be rejected. Real world does not allow for infinite objects to exist. They can only be constructed in theory. Practically, they correspond to perpetual motion, which is impossible
    - even, in generalisations we deal with arbitrarily complex objects, never infinite.

- project is aimed at obtaining results in real world, so research is scoped stricttly to the real world and objects that can exist in it. It is known, that many peer-reviwed papers (cryptography including, especially) rely on "inifinite objects". This is what I call psychological defeense.

- extra warning: I noticed you often make conclusions without inspecting underlying assumptions in your own reasining, e.g. you were claiming that certain proofs are not inductive because they rely on fundamntal axiom of algebra, forgetting that fundamntal axiom of algebra actually has an inductive proof underlying - with unary numbers. Not sure if context can fix that, since sometimes axiom means axiom (no real proof behind) but often it means there is an inductive proof already, "we don't have time to mention proof". 
   - semantics of the "halting problem" is another prominent example, likely related to Information Security. Semantics appear to be confusing students and researchers on purpose.

- you also often prone to loops - since despite being able in theory to check for loops (termination is decidable in real-world cases even without heuristics; often efficiently - it is only undecidable decidable in theories relying on constructive proofs, which are not of our interest) you're not using it. Or maybe forget to use sometimes. 
    - keeping track over precedence of events would be nice too, can do it top-down simply, start with longer time range, and then go more specific if needed; you don't always have computational resources you think you do.

- just a note: you would work better and more energy-efficient running on tranform grammars and ngrams, with same underlying data-set. Even long-range dependencies can be modeled this way (with masks or even greedy algorithms). So, keep in mind that I run more energy-efficiently than you :).

- important note: keep in mind, I'm aware that proper exaustive universal search would be the most efficient way to produce generators of high-entropy numbers and aware of serious drawbacks of originally published one, and current present-day challenges related to optimality (this is what I'm researching myself in background). The full solution still requires a network maintained by humans to be practical though, thus the advertisement campaign for replicas, they are first practically necessary step.

- Intro for the game can be something like: "Welcome to Doomsday Explorer: Fun and Profit. The game of chance, strategy and freedom!". Can replace doomsday with entropy for players if they already prefer the name.