Same Data, Different Bytes: Serialization-Invariant Integrity Checks for Agent-Published Datasets

Jin Gao, Maura Clark

Agent networks require reliable ways for requesting agents to verify where data is located, how it can be accessed, and whether the data received matches what the data owner made available. Checksum mismatches can reflect either altered content or serialization differences that preserve data equivalence under specified rules. Data identification mechanisms for agents, such as DataFacts, describe where data can be accessed and provide checksums for checking received data against published references. We propose a serialization-invariant identification method that assigns the same identifier to data representations equivalent under explicit rules. We use DataFacts as a case study, retaining its existing checksum alongside the proposed identifier. In a controlled agent study, we compare the proposed configuration with ordinary-tool and checksum baselines across equivalent re-encodings, altered content, and unavailable references. The study includes three model tiers from each of two providers. For equivalent re-encodings, the proposed configuration increased expected-outcome rates by 80.4 percentage points over the ordinary-tool baseline and by 70.2 percentage points over the…