# The Role of Summarization in Generative Agents: A Preliminary Perspective

arXiv: https://arxiv.org/abs/2305.01253 · HTML: https://mlsanity.com/paper/2305.01253/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 2 May 2023.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 8 of 8)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models](https://mlsanity.com/paper/2303.04671/) (2023)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023)
- [LLM as A Robotic Brain: Unifying Egocentric Memory and Control](https://mlsanity.com/paper/2304.09349/) (2023)
- [Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization](https://mlsanity.com/paper/2012.03502/) (2020)
- [QURIOUS: Question Generation Pretraining for Text Generation](https://mlsanity.com/paper/2004.11026/) (2020)

## Built on by (most cited first, 2 of 2)

- [StoryBox: Collaborative Multi-Agent Simulation for Hybrid Bottom-Up Long-Form Story Generation Using Large Language Models](https://mlsanity.com/paper/2510.11618/) (2025)
- [Static Sandboxes Are Inadequate: Modeling Societal Complexity Requires Open-Ended Co-Evolution in LLM-Based Multi-Agent Simulations](https://mlsanity.com/paper/2510.13982/) (2025)

## Abstract (arXiv)

Generative agents that simulate human society show tremendous potential for further research and practical applications. Specifically, the generative agent architecture comprising several meticulously designed modules constitutes the most critical component. To facilitate progress in this research, this report presents our integrated perspective on comprehending generative agents through summarization, since we believe summarization is the most fundamental and indispensable capacity of generative agents manifested across diverse scenarios. We hope this report can provide insight into understanding the importance of summarization capacity in generative agents and motivate future research.