Narrative Graph
Models hierarchical narrative memory for AI systems based on the Random Tree Model, drawing from cognitive science and statistical physics.
About
The Narrative Graph MCP offers a scientifically-grounded implementation of the Random Tree Model, a cognitive architecture designed to organize and recall narrative information within AI systems. Inspired by research in statistical physics and cognitive science, it simulates how humans encode, compress, and retrieve meaningful information across various levels of abstraction. This tool provides capabilities for hierarchical memory encoding, modeling cognitive limits with configurable parameters, generating statistical ensembles to capture population-level variance, and analyzing compression ratios and universal scaling laws in long narratives.
Key Features
- Models cognitive limits with configurable working memory parameters
- Calculates narrative compression ratios and scaling properties
- Generates statistical ensembles for recall variance analysis
- 0 GitHub stars
- Hierarchical memory encoding for narratives
- Enables flexible traversal to access summaries at different abstraction levels
Use Cases
- Encoding narrative texts into hierarchical tree structures for AI memory.
- Modeling how populations might recall narratives and analyzing variance.
- Generating summaries of narratives at user-specified levels of abstraction.
