aiqbench analysis
This research addresses a critical reliability challenge in multi-agent systems by introducing serialization-invariant identifiers that distinguish between genuine data tampering and harmless re-encoding variations. By validating this method against standard checksums across different model tiers, the study demonstrates significant improvements in verifying data integrity within agent networks without relying on specific file formats. This advancement strengthens the trustworthiness of autonomous agent ecosystems where data provenance and consistency are paramount.
Same Data, Different Bytes: Serialization-Invariant Integrity Checks for Agent-Published Datasets
arXiv:2610.24916v1 Announce Type: cross Abstract: 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 checksum baseline. The proposed configuration also increased evidence-supported rejection of altered content by 3.5 percentage points over the checksum baseline. We contribute a serialization-invariant identification method with explicit equivalence rules, an integration approach illustrated through DataFacts, and an empirical evaluation against ordinary-tool and checksum baselines.
Abstract: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 checksum baseline. The proposed configuration also increased evidence-supported rejection of altered content by 3.5 percentage points over the checksum baseline. We contribute a serialization-invariant identification method with explicit equivalence rules, an integration approach illustrated through DataFacts, and an empirical evaluation against ordinary-tool and checksum baselines.
4 pages. Accepted at the NANDA Workshop, IEEE TPS-ISA 2026
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
arXiv:2610.24916 [cs.CR]
(or arXiv:2610.24916v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2610.24916 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 319 words