Comment by dkypuros

Thanks for the thoughtful question— and good luck wrapping up the thesis!

Here’s the shortest road-map I can give for why the heavier formalism matters once you already have low-power execution nailed down.

First, the grammar + proof layer lets you guarantee properties that today’s neural LLMs can only hope to satisfy. Because every production rule carries a machine-checkable proof obligation, you can show that responses will always terminate, stay within a memory budget, or never emit strings outside a whitelisted alphabet. In practice that means the model can be certified for safety-critical or compliance-heavy settings where a probabilistic network is a non-starter.

Second, the same proofs make the system auditable and patchable by domain experts instead of ML engineers. An agronomist can inspect the maize-disease module, see that the recursion proving “all advice paths end with a referenced citation,” and swap in an updated pest table without breaking that guarantee. The edit-compile-prove cycle is minutes, not GPU-months.

Third, formal hooks open the door to hybrid workflows. You can embed the micro-LM inside a larger pipeline—say, a standard transformer model proposes a draft, and our verified core acts as a “lint pass” that repairs grammar, checks facts against a local SQLite cache, and signs the result with a proof artifact. That could be huge for regulated industries that want the creativity of big models and the certainty of formal methods.

Finally, on the speculative side, once responses are proof-carrying you can imagine device-to-device marketplaces of small, composable skills: my weather module proves bounds on forecast error, your SMS gateway proves it redacts PII, we link them and the combined proof still holds. That’s hard to do with opaque neural weights.

So the low-power story gets us in the door; the rigorous formalism is what keeps the door open when reliability, certification, or composability become the bottleneck. Hope that gives you a clearer picture—and when the thesis dust settles I’d love to hear your perspective on how formal methods could push this even further.