I noticed a pattern: every LLM framework today lets the AI manage state and do math. Then we wonder why pipelines hallucinate numbers and break at 3 AM.I took a different approach and built Aura-State, an open-source Python framework that compiles LLM workflows into formally verified state machines.Instead of hoping the AI figures it out, I brought in real algorithms from hardware verification and statistical learning:CTL Model Checking: the same technique used to verify flight control systems, now applied to LLM workflow graphs. Proves safety properties before execution.Z3 Theorem Prover: every LLM extraction gets formally proven against business constraints. If the total ≠ price × quantity, Z3 catches it with a counterexample.Conformal Prediction: distribution-free 95% confidence intervals on every extracted field. Not just "the LLM said $450k" but "95% CI: [$448k, $452k]."MCTS Routing: Monte Carlo Tree Search (the algorithm behind AlphaGo) scores ambiguous state transitions mathematically.Sandboxed Math: English math rules compile to Python AST. Zero hallucination calculations.I ran a live benchmark against 10 real-estate sales transcripts using GPT-4o-mini:
kernel.org maintainers
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Verification, testing, and specification have always been the bottleneck, not implementation. Good engineers know what they want to build. They just cannot afford to prove it correct. If that cost drops to near zero, every domain where correctness matters accelerates. Aerospace, automotive, and medical device certification currently takes years of qualification effort. Cloud providers invest similar effort qualifying security-critical services and cryptographic implementations. Verified code generation could collapse that timeline to weeks. Hardware verification, where a single bug can cost hundreds of millions of dollars, benefits equally.
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