S-ISP: Second-Order Semantic Peer Aggregation for LLM-Based Smart-Contract Vulnerability Detection

Large language models can recognize semantic smart-contract vulnerabilities, yet repeated audits often share the same confident errors: ordinary voting measures the popularity of a finding, not whether the agreement is informative. We present Soft Inverse Surprising Popularity (S-ISP), a second-order semantic-peer aggregation principle for LLM auditing. Error-driven rule evolution constructs complementary auditors with distinct operational views. S-ISP compares their continuous scores with development-estimated peer behavior under inverse states, revealing support that is unexpected under correlated errors. Bilateral reliability weights both interaction endpoints, while a margin projection preserves the frozen first-order decision. Across real-contract, project-disjoint, and cross-taxonomy evaluations with open-weight LLM backbones, S-ISP consistently improves vulnerability discrimination and probability quality over prompt-based, multi-agent, and learned aggregation baselines while reducing deployment cost. A compact two-peer deployment also halves auditor calls. Component ablations attribute the principal accuracy and calibration gains to evolved-peer construction and inverse-peer calibration, respectively. All results follow a prespecified protocol with explicit separation of training, development, and test data.