Custom semantic representation ("bryła") beats raw text in 24/27 configs — built solo on an RTX 2060, looking for feedback

Hi everyone,

I’m a self-taught builder (no CS degree — I learned this after night shifts, with AI as my tutor). I’ve been working on a custom semantic representation I call a “bryła” (Polish for “solid”), and I’d like to share results and get honest feedback.

The problem I started from: my hardware (RTX 2060, 12GB) couldn’t handle big models. LLMs kept recomputing the same meaning from scratch. So instead of waiting for better hardware, I tried a different path: what if I precompute meaning and pack it into the input?

The idea: a parser decomposes each sentence into ~28 “walls” (polarity, certainty, causality, negation, register, etc.), so the model reads structured meaning instead of guessing it from raw tokens.

What I measured (and where I was wrong first): my early results looked good, but I later found my corpus was hurting the representation — 9080 unique texts (87%) but only 483 unique structured inputs (5%). The representation saw 20× less variety than raw text. So I built a controlled, balanced test set and ran a full grid: groups {1,3,8} × model size {32,64,128} × parser noise {0,10,20%} = 27 configs.

Result: the structured input won 24/27 against raw text. At 0% noise: 100% vs 95%. Even at 20% parser noise it stayed ahead, except when the model was too small (dm=32) to learn to ignore noisy walls.

Honest caveat: this test set is synthetic — the walls are a clean signal. It’s a proof of concept that the architecture is faithful and noise-robust, NOT yet proof on natural language. That’s my next step.

My question to you: has anyone here worked on structured inputs or neurosymbolic approaches like this? How did you measure whether the structure actually adds information vs just summarizing the text? I’m especially curious how others separated “the structure helps” from “the corpus was just easy”.

Full write-up, code and measurements here: krzysiekpl/bryla-kris · Hugging Face

Thanks for reading — genuinely looking for people working on similar problems, not promotion.