[AmiteK]

That’s a reasonable hypothesis, and I agree LLMs are very good at inferring structure from raw TypeScript within a local reasoning window. However, that inference has to be repeated as context shifts or resets.

The claim I’m making is narrower: pre-processed structure isn’t about helping the model understand syntax, it’s about removing the need to re-infer relationships every time. The output isn’t a novel language - it’s a minimal, explicit representation of facts (e.g. dependencies, exports, routes) that would otherwise be reconstructed from source.

Inference works well per session, but it doesn’t give you a persistent artifact you can diff, validate, or assert against in CI. LogicStamp trades some inference convenience for explicitness and repeatability across runs.

I don’t claim one dominates the other universally - they optimize for different failure modes.

[verdverm]

> a persistent artifact you can diff, validate, or assert against in CI.

This is definitely something I don't see the value in. Why do I need to diff or look at this file in CI, it should be auto generated from the code, yea?

Why are git diffs insufficient? Models are trained on them and understand them very well. Google has some interesting research on having the models generate patches instead of code syntax directly.

I'm really not sure what pain or problem is being addressed here. Are there evals to show this concept improves overall model performance?

And to be honest, it's going to take a lot to convince me this is helpful when I can put a curated set of this same information in my AGENTS.md files I have all over my code base

> repeatability across runs

I definitely don't see how this holds up, or don't know what you mean. Are you implying your method creates repeatability if I run the same agent/LLM twice on the same input?

[AmiteK]

I think the disagreement is about where inference belongs, not whether LLMs are capable.

Git diffs + LLM inference work well for understanding changes once. What I’m targeting is reducing the need to re-infer semantic surface changes every run, especially across large refactors or long-running workflows.

Today, LogicStamp derives deterministic semantic contracts and hashes, and watch mode surfaces explicit change events. The direction this enables is treating those derived facts as a semantic baseline (e.g. drift detection / CI assertions) instead of relying on repeated inference from raw diffs.

By “repeatability” I mean the artifacts, not agent behavior: same repo state + config ⇒ same semantic model. I don’t yet have end-to-end agent performance evals versus AGENTS.md + LSP