# CIG: Measuring Conversational Information Gain in Deliberative Dialogues with Semantic Memory Dynamics

arXiv: https://arxiv.org/abs/2604.15647 · HTML: https://mlsanity.com/paper/2604.15647/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 45 references that are in the map.
- First posted on arXiv 17 Apr 2026.

## Builds on (most cited first, 18 of 18)

- [A-MEM: Agentic Memory for LLM Agents](https://mlsanity.com/paper/2502.12110/) (2025)
- [Evaluating Very Long-Term Conversational Memory of LLM Agents](https://mlsanity.com/paper/2402.17753/) (2024)
- [A Survey on the Memory Mechanism of Large Language Model based Agents](https://mlsanity.com/paper/2404.13501/) (2024)
- [Challenges in Building Intelligent Open-domain Dialog Systems](https://mlsanity.com/paper/1905.05709/) (2019)
- [MemOS: A Memory OS for AI System](https://mlsanity.com/paper/2507.03724/) (2025)
- [Evaluating Human-Language Model Interaction](https://mlsanity.com/paper/2212.09746/) (2022)
- [A Comprehensive Assessment of Dialog Evaluation Metrics](https://mlsanity.com/paper/2106.03706/) (2021)
- [Conversational flow in Oxford-style debates](https://mlsanity.com/paper/1604.03114/) (2016)
- [Six Attributes of Unhealthy Conversation](https://mlsanity.com/paper/2010.07410/) (2020)
- [Surprise! Uniform Information Density Isn't the Whole Story: Predicting Surprisal Contours in Long-form Discourse](https://mlsanity.com/paper/2410.16062/) (2024)
- [Group Communication Analysis: A Computational Linguistics Approach for Detecting Sociocognitive Roles in Multi-Party Interactions](https://mlsanity.com/paper/1801.03563/) (2018)
- [Revisiting Contextual Toxicity Detection in Conversations](https://mlsanity.com/paper/2111.12447/) (2021)
- [Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded Dog Whistles](https://mlsanity.com/paper/2406.06840/) (2024)
- [Topic-Conversation Relevance (TCR) Dataset and Benchmarks](https://mlsanity.com/paper/2411.00038/) (2024)
- [Bottom-Up Synthesis of Knowledge-Grounded Task-Oriented Dialogues with Iteratively Self-Refined Prompts](https://mlsanity.com/paper/2504.14375/) (2025)
- [Death of the Novel(ty): Beyond n-Gram Novelty as a Metric for Textual Creativity](https://mlsanity.com/paper/2509.22641/) (2025)
- [WHoW: A Cross-domain Approach for Analysing Conversation Moderation](https://mlsanity.com/paper/2410.15551/) (2024)
- [An AI-Powered Framework for Analyzing Collective Idea Evolution in Deliberative Assemblies](https://mlsanity.com/paper/2509.12577/) (2025)

## Abstract (arXiv)

Measuring the quality of public deliberation requires evaluating not only civility or argument structure, but also the informational progress of a conversation. We introduce a framework for Conversational Information Gain (CIG) that evaluates each utterance in terms of how it advances collective understanding of the target topic. To operationalize CIG, we model an evolving semantic memory of the discussion: the system extracts atomic claims from utterances and incrementally consolidates them into a structured memory state. Using this memory, we score each utterance along three interpretable dimensions: Novelty, Relevance, and Implication Scope. We annotate 80 segments from two moderated deliberative settings (TV debates and community discussions) with these dimensions and show that memory-derived dynamics (e.g., the number of claim updates) correlate more strongly with human-perceived CIG than traditional heuristics such as utterance length or TF--IDF. We develop effective LLM-based CIG predictors paving the way for information-focused conversation quality analysis in dialogues and deliberative success.