Trace Mutation in Human-LLM Dialogue: The Transcript as Forensic and Mitigation Surface
For researchers and developers of LLM-based dialogue systems, this paper identifies a novel failure mode that undermines the reliability of conversational records as decision records.
The paper characterizes a class of context failures in human-LLM dialogue called trace mutations, where distortions enter the shared conversational record while appearing as grounded continuity. Two forms are described: utterance effacement and genitive dissociation, which differ from confabulation and sycophancy and resist ordinary conversational repair.
Large language models (LLMs) are increasingly deployed as partners in knowledge work, where the shared conversational record functions as the decision record that safeguards work continuity. We characterize a class of context failures we term trace mutations, in which distortions enter the shared record while presenting as grounded continuity. We describe two forms: utterance effacement, in which an interlocutor's contribution is re-presented with altered substance, and genitive dissociation, in which a model loses authorship of its own contributions. Using a schematic illustration and two naturalistic anchor cases, we show how these failures differ from confabulation and sycophancy and why they resist ordinary conversational repair. Preliminary cross-model elicitation suggests that at least one such failure is highly camouflaged to contemporary models. We situate the phenomena within grounding and repair theory and discuss implications for tool design.