CogCanvas: Verbatim-Grounded Artifact Extraction for Long LLM Conversations
Tao An

TL;DR
CogCanvas is a training-free framework that extracts and retrieves verbatim-grounded artifacts from long LLM conversations, significantly improving accuracy over existing methods especially on complex reasoning tasks.
Contribution
Introduces CogCanvas, a training-free artifact extraction method that outperforms baselines on the LoCoMo benchmark, emphasizing its practicality and effectiveness.
Findings
Achieves highest accuracy among training-free methods (32.4%) on LoCoMo.
Outperforms RAG by +7.8 percentage points overall.
Excels in complex reasoning tasks with +20.6pp on temporal reasoning.
Abstract
Conversation summarization loses nuanced details: when asked about coding preferences after 40 turns, summarization recalls "use type hints" but drops the critical constraint "everywhere" (19.0% exact match vs. 93.0% for our approach). We present CogCanvas, a training-free framework inspired by how teams use whiteboards to anchor shared memory. Rather than compressing conversation history, CogCanvas extracts verbatim-grounded artifacts (decisions, facts, reminders) and retrieves them via temporal-aware graph. On the LoCoMo benchmark (all 10 conversations from the ACL 2024 release), CogCanvas achieves the highest overall accuracy among training-free methods (32.4%), outperforming RAG (24.6%) by +7.8pp, with decisive advantages on complex reasoning tasks: +20.6pp on temporal reasoning (32.7% vs. 12.1% RAG) and +1.1pp on multi-hop questions (41.7% vs. 40.6% RAG). CogCanvas also leads…
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Taxonomy
TopicsTopic Modeling · Speech and dialogue systems · Multimodal Machine Learning Applications
