Most approaches to AI hallucination try to make the model less likely to be wrong. But in mathematics, “less likely wrong” is not good enough. Either a proof is correct or it isn’t. Brahma V1 is a multi-agent architecture where LLMs don’t answer math questions directly — they write LEAN proofs of the answer. A formal proof compiler then decides correctness, not the model. If it compiles, it’s mathematically guaranteed. If it doesn’t, the system enters a structured retry loop with escalating LLM rotation and cumulative error memory. No hallucination can pass a formal proof compiler. That’s the core idea. Would love feedback and criticism from this community. submitted by /u/Aggravating_Sleep523
Originally posted by u/Aggravating_Sleep523 on r/ArtificialInteligence