Not every approach to AI reasoning is created equal. Here's how Jachin's neuro-symbolic architecture compares to pure LLMs, RAG pipelines, and previous symbolic AI attempts.
| Dimension | LLM | RAG + LLM | Classic Symbolic | JACHIN |
|---|---|---|---|---|
| Reasoning | Statistical | Retrieval+prediction | Logic only | Neural + symbolic fusion |
| Hallucination | Frequent | Reduced | None (brittle) | Eliminated — verified or refused |
| Explainability | Post-hoc | Source citation | Full trace | Full proof chain |
| Cross-Domain | Implicit | Document-bound | Manual re-encode | Functor mapping (auto) |
| Insufficient Data | Guesses confidently | Guesses with sources | Fails silently | Principled refusal |
Most approaches bolt logic onto neural nets as a post-processing layer — an afterthought. The reasoning is constrained by what the neural network already decided.
Built from philosophical first principles — formal ontology and category theory as native architecture. Neural perception fused with symbolic reasoning, with AI emerging its own logic from world structure.
"Not a better model. A different kind of machine."