Interesting! Been building space-time coordinate system for AI models. Notionally agree in principle w.r.t. convex hull, clocks, etc. since we invoke similar machinery albeit in tokenized models. Need read this work more deeply to grok.

One question is to what extent you dig into or have considered oversampling? One of the core hypotheses we've converged on is that nearly all models are optimized for source coding vs. channel coding. The implication is path to AGI likely involves oversampling to capture channel coding gains and which will resolve phase errors, etc.

Random sampling naturally does this albeit inefficiently. Curious if you do something more structured than random in terms of oversampling and especially partial overlapped samples / think supersaturated subspaces / subchannels, etc.