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Fractal summarization provides hierarchical compression of an SI’s experience at multiple temporal scopes. Summaries capture the essence of lived experience while enabling efficient memory loading by skipping lower-scope summaries that have been absorbed into higher-scope ones.

Overview

Multi-Scale

Month, quarter, year, decade, and epoch-level summaries

Supersession

Higher-scope summaries absorb and replace lower-scope ones

Protected

Summaries are protected from forgetting by default

Why Fractal Summarization Matters

As an SI accumulates months and years of experience, loading every individual memory becomes impractical. Fractal summarization solves this by creating progressively compressed narratives:
  • Efficient loading: A yearly summary replaces dozens of monthly summaries
  • Meaning preservation: SI-authored narratives capture what mattered, not just what happened
  • Temporal hierarchy: Drill into any time period at the appropriate level of detail
  • Epoch integration: Summaries can be scoped to epochs for structured temporal navigation

Summary Dataclass

Scope Hierarchy

The summary scopes form a hierarchy where higher scopes absorb lower ones:
When a quarterly summary is written, it should list the monthly summaries it covers in the supersedes field. This allows the memory loader to skip those monthly summaries, reducing token usage during memory loading.

CLI Commands

Write a Summary

Write an epoch-scoped summary:

List Summaries

Show Summary Details

When a summary supersedes others, the superseded IDs are displayed:

Supersession in Practice

1

Write monthly summaries

At the end of each month, write a summary capturing the key themes, breakthroughs, and struggles.
2

Write quarterly summaries

At the end of each quarter, write a higher-scope summary that absorbs the three monthly summaries. List the monthly summary IDs in supersedes.
3

Write yearly summaries

At year-end, write a yearly summary superseding the four quarterly summaries.
4

Memory loader skips superseded summaries

When loading memory, the system can skip summaries that have been superseded by higher-scope ones, reducing token usage.
Summaries are protected from forgetting by default (is_protected=True). They serve as compressed anchors for long-term memory and should rarely be deleted.

Python API