New ask Hacker News story: Aura-State: Formally Verified LLM State Machine Compiler
Aura-State: Formally Verified LLM State Machine Compiler
2 by rohanmunshi08 | 0 comments on Hacker News.
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: → 100% budget extraction accuracy ($0 mean error) → 20/20 Z3 proof obligations passed → 3/3 temporal safety properties proven → 65 automated tests passing The gap between "it usually works" and "it provably works" is smaller than people think. Would love feedback from anyone building production LLM systems; what would you want formally verified? https://ift.tt/zanNimr
2 by rohanmunshi08 | 0 comments on Hacker News.
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: → 100% budget extraction accuracy ($0 mean error) → 20/20 Z3 proof obligations passed → 3/3 temporal safety properties proven → 65 automated tests passing The gap between "it usually works" and "it provably works" is smaller than people think. Would love feedback from anyone building production LLM systems; what would you want formally verified? https://ift.tt/zanNimr
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