Trace Mutation in Human-LLM Dialogue: The Transcript as Forensic and Mitigation Surface
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913059458514944 |
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| author | Bensen, William J. |
| author_facet | Bensen, William J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22773 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Trace Mutation in Human-LLM Dialogue: The Transcript as Forensic and Mitigation Surface Bensen, William J. Human-Computer Interaction 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. |
| title | Trace Mutation in Human-LLM Dialogue: The Transcript as Forensic and Mitigation Surface |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2604.22773 |