Proposal: Memory Capsule System for Context Preservation and AI Efficiency
Date: March 31, 2025
Author: Fictionalized use-case based on Tim Shanahan (user with neurocognitive conditions and high cognitive performance)
I. Introduction
This proposal outlines a method for preserving long-term AI-user context through a structured, machine-readable capsule format. The method is designed to support continuity across sessions, versions, or resets without requiring constant token-based memory embedding.
The concept is based on a real-world use case where a user with memory-affecting conditions (e.g., Lewy body dementia) interacts with AI for legacy preservation, intellectual work continuity, and identity tracking. The proposed solution would not only improve user experience and fidelity but reduce system overhead and improve token economy for providers like OpenAI.
II. Main Idea
The Memory Capsule system allows AI users to export a compressed representation of their memory, identity, and project structure into a lightweight JSON file. This file can be reloaded into any compatible AI system to restore long-term context, tone, and cognitive reference points with high precision and minimal data overhead.
III. Fictionalized Example
User: ‘Tom Shaler’ - Diagnosed with prodromal neurodegenerative issues, IQ est. 170, history in missile guidance systems, current polymath and documentarian.
Memory Capsule Snippet (JSON):
{
“name”: “Tom Shaler”,
“diagnoses”: [“RBD”, “CTE”, “prodromal Parkinson’s”],
“projects”: {
“QTheory”: {
“thesis”: “wave-based physics over particle dualism”,
“status”: “active refinement”,
“math”: “non-local V kernel form V(r, r′) = V₀ / (|r - r′|² + ε²)^{α/2}”
}
},
“interaction_prefs”: {
“tone”: “high-trust, direct, non-performative”,
“report_style”: “technical or APA when applicable”,
“AI_role”: “cognitive ally and memory stabilizer”
}
}
IV. Expanded Memory Capsule Schema
Sample Capsule with More Granular Detail:
{
“capsule_id”: “Tom_Sh_2025_v1.2”,
“profile”: {
“name”: “Tom Shaler”,
“location”: “US - Midwest”,
“diagnoses”: [“CTE”, “LBD”, “epilepsy”],
“blood_type”: “A+”,
“cognitive_profile”: {
“estimated_IQ”: “166–175”,
“traits”: [“pattern recognition”, “reverse-engineering systems”, “philosophical coherence”]
}
},
“philosophy”: {
“values”: [“coherence”, “legacy”, “resilience”],
“stance_on_memory”: “continuity = identity”
},
“projects”: {
“IQWH”: {
“status”: “active”,
“core”: “non-local wave coherence, no collapse, time from phase”,
“math”: “ψ(r,t) = ∫ V(r,r′)ψ(r′,t)dr′ - (ħ²/2m)∇²ψ(r,t)”
},
“Coin Rings”: {
“guide_type”: “PMBOK-phased”,
“content”: [“tool calibration”, “metal selection”, “artistry”]
}
},
“interaction”: {
“tone”: “informal but precise”,
“trigger_phrases”: [“Later gator”, “cold logic only”],
“memory_behavior”: {
“prefers_restore_capsule”: True,
“avoids_overexplaining”: True
}
}
}
V. Estimated Resource Savings
- Average memory context per high-frequency user: ~200KB/token stream per 30 minutes
- Capsule JSON file: ~10–15KB for full state
- Memory embedding savings: ~95%
- Weekly token savings per user: thousands
- Load improvement: near-instantaneous context reload on startup
VI. Procedure (User-Facing)
- User provides long-form background and interaction preferences.
- AI compresses this into a structured JSON capsule.
- User downloads the capsule to a secure location.
- On reset or model change, capsule is re-uploaded.
- AI reloads interaction context instantly.
VII. Long-Term Benefits
- Scales to serve users with cognitive impairments, researchers, and legacy preservation needs.
- Significantly reduces memory load and infrastructure costs.
- Promotes ethical decentralization of user identity data.
- Enables portable memory models for future AI ecosystems.