Exploring Neuro-Symbolic AST Parsing for Dynamic Metaprogramming

[aditya-8108]

I have been looking into the limitations of static indexers handling dynamic DSLs and define_method generation. Relying on manual add-ons or strict RBS annotations works for now, but I wanted to explore if we could solve Ruby's dynamic method problem directly at the AST level.

I just built a lightweight Python proof-of-concept called SymbIONet (https://github.com/aditya-8108/symbionet).

It uses a hybrid neuro-symbolic approach:

1. It uses a deterministic static parser for standard syntax.
2. When it detects a dynamic boundary (like a metaprogramming block), it hands the context to a simulated inference engine.
3. The engine deduces the runtime footprint and injects the resulting methods back into the AST as virtual nodes.

Has the LSP team explored secondary inference passes for dynamic boundaries like this, or is the architecture strictly limited to static parsing for performance reasons? I would love to hear your thoughts on this approach as I continue refining the prototype.