Something is happening around Waluigi effect

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I am not sure what (I missed the latest part of the story). But here is a beautiful petition on change.org which says this:

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Waluigi has been scorned by Nintendo yet again, being left out of the roster of Super Smash Bros Ultimate. However, there is still a chance for Waluigi to get his rightly deserved place in the spotlight. Waluigi should appear in the next edition of Higher Algebra.

Indeed, Waluigi fits naturally into the framework of stable ∞-categories, and would probably have been incorporated long ago were Nintendo not so notoriously protective of their copyright. For example, the discussion of the Waldhausen construction in §1.2.2 generalizes without much additional effort to the WAHldhausen construction. It is also worth noting that a careful treatment of the WAHll finiteness obstruction from the ∞-categorical perspective is sorely lacking from the literature.
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(I've read the original Waluigi effect paper. I am going to write more about all this in the comments.)

Comments

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Then here https://cats.for.ai/program/ we see, for example, the above mentioned

Dynamic organizational systems: from deep learning to prediction markets
David Spivak
In training artificial neural networks (ANNs), both neurons and arbitrary populations of neurons can be seen to perform the same type of task. Indeed, at any given moment they provide a function A-->B, and given any input from A and loss signal on B, they do two things: provide an updated function A-->B and backpropagate a loss signal on A. Populations of neurons, which we called "Learners", can be put together in series or in parallel, forming a symmetric monoidal category. However, ANNs satisfy an additional property: there is a consistent method by which the functions update and errors backpropagate; namely, they all use gradient descent. The chain rule implies that the composite of gradient descenders is again a gradient descender.
In this talk I will discuss a generalization called "dynamic organizational systems", which includes ANNs, prediction markets, Hebbian learning, and strategic games. It is founded on the category Poly of polynomial functors, which generalizes Lens. I will review the relevant background on Poly and then explain dynamic organizational systems as coherent procedures by which a network of component systems can rewire its network structure in response to the data flowing through it. I'll explain the ANN case, and possibly the prediction market case, time permitting.

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And also

Category Theory Inspired by LLMs - Recording link and Slides
Tai-Danae Bradley
The success of today's large language models (LLMs) is striking, especially given that the training data consists of raw, unstructured text. In this talk, we'll see that category theory can provide a natural framework for investigating this passage from texts—and probability distributions on them—to a more semantically meaningful space. To motivate the mathematics involved, we will open with a basic, yet curious, analogy between linear algebra and category theory. We will then define a category of expressions in language enriched over the unit interval and afterwards pass to enriched copresheaves on that category. We will see that the latter setting has rich mathematical structure and comes with ready-made tools to begin exploring that structure.

https://www.youtube.com/watch?v=_LgWD3UTKfw and https://cats.for.ai/assets/slides/TDB_slides.pdf

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This one is on the paper I mentioned at the beginning of this thread, slide 4 of https://cats.for.ai/assets/slides/TDB_slides.pdf