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This paper introduces CIG (Conversational Information Gain), a framework for measuring how utterances advance collective understanding in deliberative dialogues by tracking evolving semantic memory and scoring utterances on novelty, relevance, and implication scope. The authors demonstrate that memory-derived dynamics correlate better with human-perceived dialogue quality than traditional heuristics and develop LLM-based predictors for information-focused conversation analysis.