Accidental discovery? or known method i'm missig? - Q's on compiling in sparse engrams and ideas swimming in my head

I set out to test portable Engrams and accidentally ended up testing compiled external memory instead. Am I onto something useful or reinventing a known idea? I've been building a small open research harness called tiny-sparse-lab to experiment with conditional N-gram/Engram-style memory on models small enough that I can actually run controlled tests instead of needing a datacenter. My original question was basically: If a model learns useful information in an N-gram Engram/PLE-style table, can I detach that table, freeze it, graft it onto a differently sized model with a tiny projection/gate, and recover the information? Think: Model A + trainable Engram ↓ training learned Engram ↓ export/freeze Model B + tiny adapter Model C + tiny adapter Different hidden sizes, independently trained recipients, same exact memory artifact. While building the harness for that experiment, I realized my current test had actually done something slightly different. Instead of making Model A learn the Engram values through LM training, I constructed the external memory directly from structured facts and trained small models to consume it: structured facts ↓ memory compiler ↓ frozen sparse memory ↓ small neural recipient That was not the experiment I thought I was running. :) But the bounded pilot produced an interesting pattern: correct memory 1.000 incomplete memory 0.5625 random memory 0.125 disabled memory 0.125 conflicting memory 0.000 This was only a tiny synthetic experiment, so I'm absolutely not claiming a general result . The larger portability harness then ran 120 controlled smoke arms across: token-addressed memory raw-byte-addressed memory structured semantic memory two recipient widths seeds 17/41/73 disabled/random/corrupted/frozen/adapter/joint/native-memory controls The useful part: the artifact identity checks, recipient isolation, adapter-only update auditing, memory swaps, A→B→A replay, retrieval traces, etc. all worked. The less exciting part: those were deliberately only two-update smoke tests and behavioral accuracy was 0 across the board . So that proved the experiment machinery, not portability. Which leaves me with two research questions that I now think need to be separated: 1. Learned Engram portability Train a normal N-gram memory jointly with Source Model A, export only the learned table, freeze Model B and the table, train only a tiny recipient adapter, and test whether held-out memory entries survive the transplant. Controls will include: recipient only adapter with no useful memory random memory permuted learned memory real learned memory, zero-shot real learned memory + adapter recipient-native memory This should tell me whether the memory really carries information independently of the backbone that created it. 2. Compiled memory delegation The accidental experiment might actually be more interesting to me long-term: Why make every model discover static structure through gradient descent if some of it already exists explicitly? Instead of: billions/trillions of text tokens ↓ SGD discovers facts/relations ↓ facts end up in weights + Engram could we do: Wikidata / WordNet / APIs / formulas / structured knowledge ↓ compile external sparse memory ↓ small neural model learns language + routing + composition + reasoning In other words: How much static world structure actually needs to be learned into the neural compute matrix at all? I'm not proposing that reasoning reduces to lookup. Quite the opposite. The experiment I'm interested in is whether we can separate: external memory: facts lexical relationships aliases definitions API signatures constants neural network: language context interpretation selection composition reasoning generalization and then experimentally find where that boundary breaks. One thing I particularly like about the sparse approach is that the memory can have enormous total capacity without requiring every row to sit in the active compute path. I'm eventually interested in RAM/SSD-tiered lookup rather than assuming all static knowledge needs precious GPU VRAM. But first I'm going back and running the experiment I originally meant to run: learn an Engram normally in Model A and see whether it survives being detached and grafted into independent recipients. If that works, the next experiment would be even stronger: calibrate recipient to memory interface ↓ freeze recipient ↓ attach completely unseen World B memory ↓ zero gradient updates ↓ can it reason over the new world? I'm curious what people here think: Is directly compiling structured knowledge into sparse model memory a direction anyone knows good prior work on? Is there an obvious reason learned PLE/Engram vectors should transfer better than explicitly constructed ones? For portability, what control am I missing beyond random/permuted/no-memory/matched-adapter/native-memory? Would you test multi-order N-grams next (2/3/4-gram memory allocation), or keep the mechanism intentionally simple until learned-table portability is established? Has anyone seen good work comparing “learn the knowledge through LM training” vs “supply the knowledge externally and only learn how to use it” at matched compute? Repo is supernovae/tiny-sparse-lab on GitHub if anyone wants to tear apart the methodology. Negative results are completely fine here- the whole reason I'm building the harness is that I'd rather find out an idea doesn't work at 10M–100M scale than convince myself from one cherry-picked generation that it does.