# Random Tree Model of Meaningful Memory

arXiv: https://arxiv.org/abs/2412.01806 · HTML: https://mlsanity.com/paper/2412.01806/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [From Arguments to Key Points: Towards Automatic Argument Summarization](https://mlsanity.com/paper/2005.01619/) (2020)
- [From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarization](https://mlsanity.com/paper/2306.03853/) (2023)
- [Large-scale study of human memory for meaningful narratives](https://mlsanity.com/paper/2311.04742/) (2023)
- [Fundamental Law of Memory Recall](https://mlsanity.com/paper/1905.02403/) (2019)
- [Synaptic Theory of Chunking in Working Memory](https://mlsanity.com/paper/2408.07637/) (2024)

## Built on by (most cited first, 4 of 4)

- [Memory-Augmented Transformers: A Systematic Review from Neuroscience Principles to Enhanced Model Architectures](https://mlsanity.com/paper/2508.10824/) (2025)
- [Semantic Chunking and the Entropy of Natural Language](https://mlsanity.com/paper/2602.13194/) (2026)
- [AlphaContext: An Evolutionary Tree-based Psychometric Context Generator for Creativity Assessment](https://mlsanity.com/paper/2604.18398/) (2026)
- [Dark Speculation: Combining Qualitative and Quantitative Understanding in Frontier AI Risk Analysis](https://mlsanity.com/paper/2511.21838/) (2025)

## Abstract (arXiv)

Traditional studies of memory for meaningful narratives focus on specific stories and their semantic structures but do not address common quantitative features of recall across different narratives. We introduce a statistical ensemble of random trees to represent narratives as hierarchies of key points, where each node is a compressed representation of its descendant leaves, which are the original narrative segments. Recall is modeled as constrained by working memory capacity from this hierarchical structure. Our analytical solution aligns with observations from large-scale narrative recall experiments. Specifically, our model explains that (1) average recall length increases sublinearly with narrative length, and (2) individuals summarize increasingly longer narrative segments in each recall sentence. Additionally, the theory predicts that for sufficiently long narratives, a universal, scale-invariant limit emerges, where the fraction of a narrative summarized by a single recall sentence follows a distribution independent of narrative length.