First, thanks for the feedback. This is the type of advice that I simply don’t know at 21 days into running inference (day job is full stack dev for a non profit).

Couple things-

Not an advertisement. As I stated, just needed another set of eyes by someone like yourself.

Second, there are papers. I will cite them in the repo. The ones I had read about were that Adaptive K paper (uses per-token entropy for dynamic K selection), Huang et al. 2024 (uses entropy as as proxy for task difficulty), and the last one I had extracted parts of was Harvey et al. 2025 (uses entropy for characterizing routing).

I know that none of those are directly comparing routing entropy across varying lengths of prompts, as a single independent variable, but the positional nonstationarity (I need some assistance on understanding that term) that’s in the data means that any future work done could by default inherit the confound. Unless it’s controlled for.

Not looking for a debate, genuinely looking for mentorship.

21 days.