The Chronicles of Foundation AI for Forensics of Multi-Agent Provenance

2025-04-17

AI Summary

In multi-agent generative chains, content evolution traces are often lost, making it difficult to reconstruct temporal origins solely from final outputs. Method: This paper proposes a post-hoc attribution method that requires neither internal state access nor external metadata. Its core innovations are: (i) a symbolic “chronological ledger” mechanism that couples generation operations with timestamped digital signatures to form a judicial-style chain-of-custody structure; and (ii) a “generate-and-synchronize” feedback-driven provenance paradigm, integrating symbolic logic modeling with a post-hoc verifiable attribution algorithm. Contribution/Results: Evaluated on multi-round collaborative generation tasks, the method enables end-to-end historical reconstruction, supports fine-grained contribution attribution, and detects unauthorized modifications—thereby establishing an auditable, accountable foundational provenance capability for collaborative AI systems.

Technology Category

• Natural Language Processing: Generation
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Security and Privacy: Data transparency and provenance

Abstract

Provenance is the chronology of things, resonating with the fundamental pursuit to uncover origins, trace connections, and situate entities within the flow of space and time. As artificial intelligence advances towards autonomous agents capable of interactive collaboration on complex tasks, the provenance of generated content becomes entangled in the interplay of collective creation, where contributions are continuously revised, extended or overwritten. In a multi-agent generative chain, content undergoes successive transformations, often leaving little, if any, trace of prior contributions. In this study, we investigates the problem of tracking multi-agent provenance across the temporal dimension of generation. We propose a chronological system for post hoc attribution of generative history from content alone, without reliance on internal memory states or external meta-information. At its core lies the notion of symbolic chronicles, representing signed and time-stamped records, in a form analogous to the chain of custody in forensic science. The system operates through a feedback loop, whereby each generative timestep updates the chronicle of prior interactions and synchronises it with the synthetic content in the very act of generation. This research seeks to develop an accountable form of collaborative artificial intelligence within evolving cyber ecosystems.

Problem

• Tracking multi-agent provenance in generative chains
• Attributing generative history from content alone
• Developing accountable collaborative AI in cyber ecosystems

Innovation

• Chronological system for post hoc attribution
• Symbolic chronicles with signed time-stamped records
• Feedback loop updating chronicles during generation