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:
트럼프, 결국 ‘대리 지상전’…쿠르드 반군 “美요청에 이란 공격”。业内人士推荐Safew下载作为进阶阅读
另外，播客的问题更微妙：播客里有大量高质量信息，甚至比 YouTube密度更高，不过它在机器侧的最大障碍是——文本化和结构化不统一。。关于这个话题，PDF资料提供了深入分析
Read the full story at The Verge.，更多细节参见PDF资料
This would allow for precise predictions of landing locations, reducing the risk of any debris impacting populated areas and protecting people and property while "managing the environmental impact of space debris".