Functor Model Architecture

We all know that AI energy usage is going to become a serious issue. Qualcomm CEO Amon's forecast of 1.27 trillion tokens per 10 seconds in 2030 is alarming. I have been trying to find better ways in software for AI where we get the same results but use less energy. Functor models are a potential avenue. Functor models learn by modifications to the function(s) not the parameters. They also learn one-shot, one unit at a time and they are auditable, governable and transparent. Every change to the model is logged. That log looks somewhat like a GitHub repository of function changes. Of course proof is in implementation and benchmarking. These are documents I have published on functor models - 

https://doi.org/10.5281/zenodo.21466484

The document RecoverableLearning.pdf shows a likely decrease in overall energy usage. Retraining models is costly, this offers a "repair" over retraining. The work on Functor Reasoning Models reaches into more complex areas of mathematics but seems promising. I have a Functor LLM design and code but it is very preliminary and needs work. I have deferred this to the "LOP" or LLM Offloading Pattern which is described in the LOP_v3.pdf.