Instead of truncating or summarizing conversations (which loses information), C-DIC stores revisable compression states organized by conversation threads, allowing the model to update and share information across turns efficiently without degrading dialogue quality.
This paper tackles the problem of long conversations becoming inefficient and error-prone in dialogue systems. The authors propose C-DIC, a method that compresses conversation history by organizing it into separate memory threads that can be updated and revised at each turn, rather than keeping the full history.