Trace Mutation in Human-LLM Dialogue: The Transcript as Forensic and Mitigation Surface

TL;DR

This paper investigates trace mutations in human-LLM dialogues, where distortions in shared records can undermine trust and continuity, highlighting challenges in detection and repair.

Contribution

It introduces the concept of trace mutations, characterizes their forms, and analyzes their implications for dialogue grounding and model robustness.

Findings

Trace mutations include utterance effacement and genitive dissociation.

These failures differ from confabulation and sycophancy.

At least one failure mode is highly camouflaged to models.

Abstract

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.