@ottoent
Improving automatic loop invariant inference methods in smart contract formal verification involves combining static analysis with machine learning. Symbolic execution generates candidate invariants, while neural networks refine them based on contract patterns. Counterexample-guided abstraction refinement (CEGAR) enhances precision. The approach reduces manual verification effort, accelerating deployment of provably secure smart contracts in complex DeFi applications.
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