Questioning Representational Optimism in Deep Learning

The full title of the paper is “Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis.”

https://arxiv.org/abs/2505.11581

This is an interesting paper. It's nice to see AI research addressing some of the implied assumptions that compute-scale focused initiatives are relying on.

A lot of the headline advancements in AI place lots of emphasis on model size and training dataset size. These numbers always make it into abstracts and press releases and especially for LLMs even cursory investigation into how outputs are derived from inputs through different parts of the model is completely waved off with vague language along the lines of manifold hypothesis or semantic vectors.

This section stands out: "However, order cannot be everything—humans seem to be capable of intentionally reorganizing information through reanalysis or recompression, without the need for additional input data, all in an attempt to smooth out [Fractured Entangled Representation]. It is like having two different maps of the same place that overlap and suddenly realizing they are actually the same place. While clearly it is possible to change the internal representation of LLMs through further training, this kind of active and intentional representational revision has no clear analog in LLMs today."

Don't editorialize. Title is: "The Fractured Entangled Representation Hypothesis"

@dang

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