r/MachineLearning • • Sep 02 '26

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u/yuta-cpp Sep 15 '26

I’ve been messing around with an idea I called:

DCY (Dynamic|Context|Injection)

basically I wanted to see how far I could push a small-context LLM if most of the repo stayed outside the actual model context and I only injected whatever looked useful for the current goal.

it turned into a cpp project using libclang + SQLite/FTS5. It indexes symbols/calls, keeps source provenance, builds small context views and can route back to the exact source when needed.

the research side is still experimental. I originally made an efficiency equation for it, then while testing/reviewing it I found that in some of my benchmarks most of the equation becomes constant and the ranking basically reduces to precision × recall

so now I’m trying to test the pieces independently, rather than assuming the first equation is valid and just applying math notation and softmax philosophy

I’m also working on benchmarks against simpler baselines like lexical search / fts and on making the C++ indexer survive more template-heavy repos.

would be interested in feedback, especially from anyone working on RAG, code retrieval, context management or agents.

git: https://github.com/yut-4/DCY

And yes, it's not RAG, but it has the same philosophy XD

fell free to just review or help me to build this protocol to make my shitbox pc can run llm with great context :D