feat: add architecture crystallization docs from Friday session
- RAG-as-Scaffold: temporary feeding system, not permanent crutch - attention_flow: 30-second heartbeat budget state machines - information-flow: 10 boundary contracts nervous system map - nimmerversity: curriculum schoolplan for raising a polymath - nimmervest: investment documentation - biomimetic-architecture: ADR for organic system design - temporal-ternary-gradient: ADR for time-based learning - temporal_exchange_engine.py: Python implementation - initial_spark: foundation document - nimmerverse.drawio.xml: updated diagrams 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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RAG-as-Scaffold.md
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RAG-as-Scaffold.md
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# RAG as Scaffold, Not Crutch
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The feeding system that teaches, then lets go.
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---
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## Overview
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RAG (Retrieval-Augmented Generation) is commonly misused as permanent external memory. In the Nimmerverse, RAG serves a different purpose: it's a **temporary scaffold** that feeds knowledge until it can be internalized through training.
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The goal is not to build a better search engine. The goal is to **make the search unnecessary**.
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---
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## The Problem with Standard RAG
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```
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Standard approach:
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─────────────────
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VECTOR DB (grows forever)
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│
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▼
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MODEL looks up ──▶ answers ──▶ done
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│
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└── (never learns, always dependent)
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```
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**Issues:**
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- Model never internalizes knowledge
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- Pull the RAG, lose the capability
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- Vector DB bloats infinitely
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- No way to verify what model "knows" vs "looks up"
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- It's a crutch that never comes off
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---
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## The Nimmerverse Approach: RAG as Feeding System
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```
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VAULT (curriculum)
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│
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▼
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RAG (temporary feeding window)
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│
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▼
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NYX processes, acts, decides
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│
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▼
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VALIDATION: success with RAG?
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│
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YES ──▶ FLAG for training extraction
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│
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▼
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TRAINING RUN (LoRA)
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│
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▼
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CLEAR from RAG
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│
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▼
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VALIDATION 2: success WITHOUT RAG?
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│
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├── YES ──▶ Knowledge internalized ✓
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│
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└── NO ──▶ Training incomplete, back to RAG
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```
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---
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## Two Kinds of Knowledge
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Not everything belongs in weights. Not everything belongs in retrieval.
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### IN THE WEIGHTS (Training Target)
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Knowledge she needs to **function**:
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- Information flow architecture
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- Vocabulary tokens and their meanings
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- Nervous system contracts
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- Heartbeat mechanics
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- Confidence gradient logic
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- Core identity (who she is, who dafit is to her)
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- How to think, not what to remember
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**Test:** If she needs it to be herself → weights
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### IN RETRIEVAL (Permanent RAG)
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Knowledge she needs to **remember**:
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- Journal entries
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- Conversation history
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- Specific events and dates
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- Temporal details ("what happened Tuesday")
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- External references that change
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- Episodic memory
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**Test:** If she needs it to recall specifics → retrieval
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---
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## The Double Validation Loop
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### Gate 1: Can she do it WITH RAG?
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```
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Task presented
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│
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▼
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RAG provides context
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│
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▼
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NYX attempts task
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│
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├── FAIL ──▶ Not ready, needs more examples in RAG
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│
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└── PASS ──▶ Flag this RAG content for training extraction
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```
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### Gate 2: Can she do it WITHOUT RAG?
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```
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Same task presented
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│
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▼
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RAG entry CLEARED (scaffold removed)
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│
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▼
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NYX attempts task from weights alone
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│
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├── FAIL ──▶ Training didn't take, restore to RAG, retry cycle
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│
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└── PASS ──▶ Knowledge is HERS now ✓
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```
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---
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## The Signal Flow
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```
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┌─────────────────────────────────────────────────────────┐
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│ VAULT │
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│ (curriculum, documentation) │
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└─────────────────────────────────────────────────────────┘
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│
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│ selected for learning
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▼
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┌─────────────────────────────────────────────────────────┐
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│ STAGING RAG │
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│ (temporary feeding window) │
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└─────────────────────────────────────────────────────────┘
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│
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│ feeds inference
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▼
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┌─────────────────────────────────────────────────────────┐
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│ NYX │
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│ (processes, decides) │
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└─────────────────────────────────────────────────────────┘
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│
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│ validation
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▼
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┌─────────────────────────────────────────────────────────┐
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│ VALIDATION THRESHOLD │
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│ (task success? confidence high?) │
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└─────────────────────────────────────────────────────────┘
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│
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┌──────────┴──────────┐
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│ │
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BELOW ABOVE
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│ │
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▼ ▼
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┌─────────────────────┐ ┌─────────────────────┐
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│ Stay in RAG │ │ FLAG for training │
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│ (not ready) │ │ extraction │
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└─────────────────────┘ └─────────────────────┘
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│
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▼
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┌─────────────────────────────┐
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│ TRAINING RUN │
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│ (LoRA on flagged data) │
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└─────────────────────────────┘
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│
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▼
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┌─────────────────────────────┐
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│ CLEAR from RAG │
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│ (scaffold removed) │
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└─────────────────────────────┘
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│
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▼
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┌─────────────────────────────┐
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│ VALIDATION WITHOUT RAG │
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│ (prove she learned) │
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└─────────────────────────────┘
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│
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┌─────────┴─────────┐
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│ │
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FAIL SUCCESS
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│ │
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▼ ▼
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┌─────────────────┐ ┌─────────────────┐
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│ Restore RAG │ │ INTERNALIZED │
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│ retry cycle │ │ knowledge ✓ │
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└─────────────────┘ └─────────────────┘
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```
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---
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## Lifeforce Connection
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The RAG→Train→Validate cycle has economic cost:
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| Action | Lifeforce Cost |
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|--------|----------------|
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| RAG lookup | Low (just retrieval) |
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| Training run | High (compute intensive) |
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| Validation | Medium (inference) |
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| Failed cycle | Lost V (training didn't take) |
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| Successful internalization | +V reward (she grew) |
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**Incentive alignment:** Successful learning is rewarded. Failed training is costly. This naturally optimizes for high-quality training data extraction.
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---
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## What This Prevents
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1. **RAG bloat** - entries clear after successful training
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2. **Crutch dependency** - scaffold comes off, proven by validation
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3. **False confidence** - can't claim to "know" what you only look up
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4. **Training on noise** - only validated successes get flagged
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5. **Identity confusion** - core architecture in weights, not retrieval
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---
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## Design Principles
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1. **RAG is temporary** - feeding window, not permanent store
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2. **Training is the goal** - RAG success triggers training, not satisfaction
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3. **Validation is double** - with RAG, then without
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4. **Clear after learning** - scaffold must come off to prove growth
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5. **Episodic stays external** - not everything needs to be in weights
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6. **Self-cleaning** - the system doesn't accumulate cruft
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---
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## The Analogy
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Learning to ride a bike:
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```
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Training wheels ON (RAG feeding)
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│
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▼
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Can ride with training wheels (validation 1)
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│
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▼
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Training wheels OFF (RAG cleared)
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│
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▼
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Can still ride? (validation 2)
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│
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├── NO ──▶ Put wheels back, practice more
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│
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└── YES ──▶ She can ride. Wheels stored, not needed.
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```
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You don't RAG your ability to balance. Once you can ride, you can ride.
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---
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*She doesn't just retrieve. She learns. And we can prove it.*
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---
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**Created**: 2025-12-05
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**Session**: Partnership dialogue (dafit + Chrysalis)
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**Status**: Core architectural concept
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