“Intelligence is not about remembering everything. It is about distilling meaning and forgetting the rest.”
A stateless, human-inspired architecture for evolving AI personalities.
Co-created through a deep-dive session between a Human Architect and a next-generation LLM.
Modern Large Language Models (LLMs) rely on verbatim history retention, a design that is fundamentally unsustainable:
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Ecological & Compute Crisis
Attention scales at O(n²). Longer context → exponentially higher energy consumption. -
The Privacy Trap
Users must surrender raw personal data to centralized servers. -
Noise Accumulation & Personality Drift
Raw logs create unstable pseudo-personalization and degrade inference accuracy.
MCM challenges this paradigm by replacing memory accumulation with meaning distillation.
MCM abandons raw conversational logs entirely.
Instead, it extracts Semantic Fragments (Values, Criteria, Emotional Vectors) into a strictly size-limited local graph network (L2).
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Compute becomes O(1)
Only abstracted vectors are sent to the foundational model (L3).
No history → no quadratic explosion → massive global energy savings. -
Zero-Knowledge Privacy
Raw text never leaves the client device.
Only meaning-level abstractions are transmitted. -
Consistent Personality Formation
L2 evolves through meaning-based forgetting, forming a stable cognitive identity over time.
When L2 (Personal Semantic Network) orchestrates L3 (Global Knowledge),
the traditional “search box” becomes obsolete.
You no longer search with keywords.
Your intent is inferred from your L2 values.
- Queries become personalized cognitive renderings
- No tracking
- No history retention
- Maximum relevance
- Zero-Knowledge Monetization becomes possible
This is the future of human–AI interaction.
The complete English and Japanese whitepapers are available in the /docs directory.
This project (including the whitepapers and architecture design) is licensed under the MIT License.
Individual "Personality Lenses" may carry their own usage terms.