I’m actually building the system-level approach this memo hints at. I’m not from a lab or an academic group, but I’ve been working on a post-transformer infere

I’m actually building the system-level approach this memo hints at. I’m not from a lab or an academic group, but I’ve been working on a post-transformer inference method where you extract a low-rank “meaning field” from a frozen Llama-70B layer and train a small student model to generate those fields directly. The idea is similar to what this memo describes, but with an empirical implementation. I just open-sourced the reference version here: GitHub: https://github.com/Anima-Core/an1-core https://github.com/Anima-Core/an1-core Paper + DOI: https://zenodo.org/records/17873275 https://zenodo.org/records/17873275 It isn’t about bigger models. It’s about reorganizing the system around meaning and structure, then treating the transformer as a teacher rather than the final destination. I’d genuinely appreciate critique or replication attempts from people here. HN tends to give the most honest feedback.