CogCanvas: Verbatim-Grounded Artifact Extraction for Long LLM Conversations

This paper introduces CogCanvas, a system designed for verbatim-grounded artifact extraction from extensive Large Language Model (LLM) conversations. It addresses the challenge of managing and leveraging information from long dialogues by precisely identifying and extracting key artifacts, such as facts, decisions, and action items, enhancing the utility and interpretability of LLM interactions for practical applications in various domains.