How to track multi-agent content origins?
The Chronicles of Foundation AI for Forensics of Multi-Agent Provenance
This paper proposes a system for tracking the contributions of individual AI agents in collaborative content creation, specifically for text generation. It uses "chronicles," or timestamps of contributing agents, which are encoded directly into the generated text through subtle lexical biases. This eliminates the need for external metadata and allows provenance to be recovered directly from the final text output. Key points for LLM-based multi-agent systems include the scalable creation of codebooks for managing lexical biases, the feedback loop for updating and encoding chronicles during generation, and the decoding process for reconstructing the order of agent contributions. Experiments demonstrate the tradeoff between accurate provenance tracking (achieved through stronger bias) and the quality of generated text (which decreases with stronger bias).