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The article suggests that AI agents are often built with an implicit assumption of low-dimensional behavior, but the actual space they operate in is much higher-dimensional, leading to unexpected complexities.
The tweet critiques standard audio VAEs for using a uniform Gaussian prior on all latent channels, ignoring the structured nature of low frequencies and chaotic high frequencies, a problem termed 'disordered information packing.'
The author argues that treating video as stateless input (i.e., without temporal context) is a limitation that holds back AI agents, and suggests that stateful video processing could improve performance.
This thread argues that standard transformers have a topological flaw: once a state representation reaches the top layer, they cannot update beliefs over time, causing collapse as depth increases.