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Policy-Controlled Offline-Verifiable Accountability for Cross-Organization Agent Messaging: A Preserved Evidence-Bundle Approach
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arXiv:2608.28542v2 Announce Type: replace-cross Abstract: Cross-organization agent workflows require preserved evidence that remains independently verifiable during later audit or dispute review. Such workflows may involve multiple organizations, delegated actions, policy-relevant events, and disputed accountability claims. This is difficult when live systems are unavailable, controlled by one party, or not trusted by all participants. Existing mechanisms provide useful pieces, including authenticated logging, delegation semantics, signed checkpoints, and consistency checks. What remains missing is a verifier-centered event-level bundle for checking evidence sufficiency offline under an explicit policy. We propose a preserved evidence-bundle model and a policy-controlled offline verifier for agent-to-agent workflow events. Each bundle preserves policy-required evidence, including sender authentication, authenticated log commitment, witness-backed checkpoint evidence, append-only continuity, delegation-aware authorization evidence, and, when issued and required by policy, explicit receiver-signed receipt evidence. The verifier accepts a bundle only when all mandatory and policy-required checks succeed, giving a later dispute reviewer an offline basis for assessing evidence sufficiency. It does not infer delivery or receipt from transport behavior or log inclusion alone. In a prototype evaluation, the event-type study used 300 synthetic four-event healthcare workflow cases and 1200 valid preserved bundles. The policy-profile comparison observed its largest median latency reduction relative to the fully enabled profile when checkpoint-context validation was disabled. All 17 tested cases in the original negative-evidence diagnostic suite were rejected under the fully enabled profile. Additional functional studies examined context and signed-reference binding. These results support prototype feasibility for the evaluated workloads.
Source:https://arxiv.org/abs/2608.28542PreviousAutomated Root-Cause Subclassification and No-Code Fix Generation for Invalid Bug ReportsNextPA-SaR: Perception-Aware Learning, Control, and Coordination for Multi-Robot Search and Rescue
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