Large language model (LLM) interaction records are increasingly vital in digital forensics and compliance auditing. However, traditional linear tamper-evident logs fail to capture the inherent non-linear evolution of LLM conversations, such as re-prompting based on historical queries, response regeneration, session deletion, multi-device concurrency, and selective sharing. To address this issue, this paper proposes Verifiable Conversation Transc...

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