(on of the creators of BAML here) yep! exactly!

that workaround we've found works quite well, but the problem is that its not sufficient to just retry in the case of failed schema matches (its both inefficient and also imo incorrect).

Take these two scenarios for example:

Scenario 1. My system is designed to output receipts, but the user does something malicious and gives me an invoice. during step 2, it fails to fit the schema, but then you try with step 3, and now you have a receipt! Its close, but your business logic is not expecting that. Often when schema alignment fails, its usually because the schema was ambiguous or the input was not valid.

Scenario 2. I ask the LLM to produce this schema:

    class Person {
      name string
      past_jobs string[]
    }

However the person only has ever worked at 1 job. so the LLM outputs: { "name": "Vaibhav", "past_jobs": "Google" }. Technically since you know you expect an array, you could just transform the string -> string[].

thats the algorithm we created: schema-aligned parsing. More here if you're interested: https://boundaryml.com/blog/schema-aligned-parsing

Benchmark wise, when we tested last, it seems to help on top of every model (especially the smaller ones) https://www.reddit.com/r/LocalLLaMA/comments/1esd9xc/beating...

Hope this helps with some of the ambiguities in the post :)