We Found a Failure Mode in AI Summarization After Two Reddit Users Challenged the Reconstruction Logic

A couple people in my last thread pointed out important edge cases around chronology reconstruction and duplicate-looking records, so I updated the system and reran the methodology. The core issue: Standard AI summarization tends to normalize and flatten records early. That works fine until: \- chronology matters \- contradictory statements exist \- or the same communication appears in multiple contexts with different evidentiary meaning One example from the updated reconstruction: An original approval email, a forwarded copy of that same email, and a later invoice referencing that approval all looked superficially similar. A normal summarizer tends to collapse them into one event. But they are not actually the same thing. The forwarded version changed the evidentiary meaning because it captured internal uncertainty after the alleged approval occurred. So the system now preserves: \- chronology \- contradiction context \- duplicate-looking but distinct records \- confidence levels \- and decision weighting instead of flattening everything into a clean narrative too early. Current demo: https://www.notion.so/What-Actually-Happened-Standard-AI-vs-Source-Backed-Chronology-357c42abce4080c9832ecba60617eaa2?source=copy\_link Still looking for edge cases, failure modes, and places where the reconstruction logic breaks down.