When Only the Final Text Survives: Implicit Execution Tracing for Multi-Agent Auditing

arXiv:2603.17445v5 Announce Type: replace Abstract: When a multi-agent system produces an incorrect or harmful answer, who is accountable if execution logs and agent identifiers are unavailable? In practice, generated content is often detached from its execution environment due to privacy or system boundaries, leaving the final text as the only auditable artifact. Existing attribution methods rely on full execution traces and thus become ineffective in such metadata-deprived settings. We propose Implicit Execution Tracing (IET), a provenance-by-design framework that shifts attribution from post-hoc inference to built-in instrumentation. Rather than inferring provenance after the fact, IET embeds agent-specific, key-conditioned statistical signals into the token generation process at generation time, turning the output text into a self-verifying provenance record. An offline auditor, holding a verification registry that maps each agent to its key, then recovers segment-level provenance - segment boundaries and per-segment agent attribution - from the final text alone without access to execution logs or private traces. Experiments across diverse multi-agent coordination settings demonstrate that IET achieves accurate segment-level attribution and reliable transition recovery under identity removal, boundary corruption, and privacy-preserving redaction, while maintaining generation quality. These results show that embedding provenance into generation provides a practical foundation for accountability in multi-agent language systems under metadata loss.

Abstract:When a multi-agent system produces an incorrect or harmful answer, who is accountable if execution logs and agent identifiers are unavailable? In practice, generated content is often detached from its execution environment due to privacy or system boundaries, leaving the final text as the only auditable artifact. Existing attribution methods rely on full execution traces and thus become ineffective in such metadata-deprived settings. We propose Implicit Execution Tracing (IET), a provenance-by-design framework that shifts attribution from post-hoc inference to built-in instrumentation. Rather than inferring provenance after the fact, IET embeds agent-specific, key-conditioned statistical signals into the token generation process at generation time, turning the output text into a self-verifying provenance record. An offline auditor, holding a verification registry that maps each agent to its key, then recovers segment-level provenance - segment boundaries and per-segment agent attribution - from the final text alone without access to execution logs or private traces. Experiments across diverse multi-agent coordination settings demonstrate that IET achieves accurate segment-level attribution and reliable transition recovery under identity removal, boundary corruption, and privacy-preserving redaction, while maintaining generation quality. These results show that embedding provenance into generation provides a practical foundation for accountability in multi-agent language systems under metadata loss.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2603.17445 [cs.AI]

(or arXiv:2603.17445v5 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2603.17445 Focus to learn more arXiv-issued DOI via DataCite

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When Only the Final Text Survives: Implicit Execution Tracing for Multi-Agent Auditing

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