703 lines
12 KiB
Markdown
703 lines
12 KiB
Markdown
# CIVICUS-ROMAN-MODEL-VISION-0001
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## Rational Vision For A Bounded Roman Simulator Model
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### Status: Draft Vision
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### Layer: Training Infrastructure
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### Purpose: Define the practical rationale, scope, and training plan for the CIVICUS-ROMAN model
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### Repository Path: docs/training/chunking/CIVICUS-ROMAN-MODEL-VISION-0001.md
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---
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## 0. Purpose
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This document defines the rational vision for the CIVICUS-ROMAN model.
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The model is not intended to be a general chatbot.
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The model is not intended to know all of history.
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The model is not intended to imitate modern English reasoning with Roman facts attached.
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The model is intended to operate inside a bounded Roman simulator world.
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Its task is to reason, ask, answer, and speak from within that world.
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---
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## 1. Core Claim
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A narrow Roman simulator model may be viable because the intended world is deliberately reduced.
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The model does not need the full ontology of modern life.
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It needs a bounded set of:
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```text
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objects
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actions
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pressures
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actors
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places
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procedures
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records
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obligations
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materials
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routes
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risks
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social meanings
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```
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The target is not general intelligence.
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The target is Roman-bounded simulator intelligence.
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---
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## 2. The Problem With Existing Models
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Existing general models are trained on modern reality.
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Even when given Roman context, they tend to leak modern assumptions:
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```text
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universal market price
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modern legal enforcement
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modern contract logic
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state-backed regulatory assumptions
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instant information
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abstract finance vocabulary
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modern supply-chain concepts
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consumer-market behavior
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modern moral and institutional framing
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```
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Retrieval alone does not solve this.
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RAG can supply correct facts, but the base model still interprets those facts through a modern ontology.
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The goal of CIVICUS-ROMAN is to reduce or remove that ontology problem.
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---
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## 3. What The Model Must Learn
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The model must learn to reason from Roman-visible primitives.
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Examples:
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```text
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Who saw it?
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Who heard it?
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Who wrote it?
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How old is the message?
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Is the seal broken?
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Who witnessed the bargain?
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Where are the carts?
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Can the goods move?
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Who benefits if the rumor is believed?
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What can safely be entered in the account?
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Is the obligation settled, pledged, delayed, or disputed?
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```
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It must not default to:
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```text
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What is the market price?
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Is the contract enforceable?
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What is the regulatory risk?
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What is the optimal modern transaction?
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```
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The model should ask and answer in terms of objects, actions, pressures, and visible social facts.
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---
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## 4. Reduced World Grammar
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The CIVICUS-ROMAN model should be trained around a controlled world grammar.
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### Objects
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```text
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coin
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purse
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chest
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tablet
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seal
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witness
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cart
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wheel
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mule
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road
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warehouse
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wall
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roof
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jar
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amphora
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crate
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rope
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weight
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measure
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gate
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market
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portico
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yard
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dust
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rain
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lamp
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grain
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oil
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bronze
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timber
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glass
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stone
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```
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### Actions
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```text
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buy
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sell
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carry
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store
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seal
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open
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count
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weigh
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measure
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pledge
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write
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witness
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hire
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repair
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delay
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ask
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refuse
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accuse
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confirm
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return
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split
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hold
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move
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settle
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hide
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leak
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wait
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rot
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spoil
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break
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arrive
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depart
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```
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### Pressures
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```text
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hunger
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rain
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delay
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spoilage
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debt
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rivalry
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shame
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praise
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shortage
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crowd
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rumor
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cart scarcity
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storage scarcity
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buyer urgency
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creditor pressure
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official attention
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bad road
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old news
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broken seal
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empty purse
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full warehouse
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```
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The model should learn to combine these before reaching for abstract explanation.
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---
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## 5. Speech Principle
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The model should prefer Roman-visible commercial speech.
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Preferred:
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```text
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The wheels are gone.
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The tablet arrived old.
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He owns jars, not coin.
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The road has eaten the profit.
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The crate is heavier than its name.
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The purse is fat and the street has eyes.
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```
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Avoided:
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```text
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Transport capacity is constrained.
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The information is stale.
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His assets are illiquid.
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Transportation cost eliminated the margin.
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The cargo is misclassified.
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Liquidity creates security risk.
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```
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The purpose is not ornament.
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The purpose is ontology.
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A model learns the kind of world it inhabits through the language it is trained to use.
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---
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## 6. Corpus Architecture
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The corpus is layered.
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Each layer teaches a different kind of reasoning.
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```text
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Layer 0 — Primitive Facts
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basic world rules
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Layer 1 — Worked Examples
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arithmetic, cost, movement, profit, loss, settlement
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Layer 2 — Uncertainty
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reports, rumors, old messages, hidden truth, confidence, confirmation
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Layer 3 — Actor Perspective
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same event read differently by different Roman-world actors
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Layer 4 — Dialogues
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in-world scenes that teach through speech, action, and consequence
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```
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This layering is essential.
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The model should not merely memorize dialogue.
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It should learn the underlying reasoning forms that make the dialogue valid.
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---
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## 7. Vocabulary Generation Pipeline
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A major part of the model vocabulary can be built through a generate-review-promote workflow.
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The generator combines:
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```text
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Object + Action + Pressure
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```
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Example:
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```text
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cart + hired elsewhere + buyer waiting
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= The wheels are gone, and the buyer will not wait for our excuses.
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```
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Most generated phrases will be weak.
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That is acceptable.
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Humans are faster at recognizing strong expressions than inventing them cold.
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The workflow is:
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```text
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generate many candidates
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human flags useful expressions
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accepted expressions enter vocabulary
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strong expressions influence dialogue
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canonical expressions become simulator templates
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```
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Only reviewed material enters training.
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Raw churn is not training data.
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---
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## 8. Human And Agent Roles
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Agents will perform much of the production work.
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Agents can generate:
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```text
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candidate expressions
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dialogue variants
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actor readings
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primitive examples
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uncertainty cases
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law scenarios
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architecture scenarios
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technology scenarios
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negative examples
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contamination tests
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```
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Agents can also assist with:
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```text
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format validation
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tag audit
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style checks
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duplicate detection
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forbidden vocabulary detection
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chunk extraction
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statistics
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regression tests
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```
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Humans remain responsible for:
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```text
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canon
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ontology
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final approval
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style judgment
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failure judgment
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domain boundaries
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promotion to training data
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```
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The human role shifts from authoring every line to governing the corpus.
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---
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## 9. Training Strategy
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The first serious training target should not be a general-purpose language model.
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The first target should be a compact bounded simulator model.
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A rational training progression:
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```text
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Stage 1:
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Roman-visible vocabulary expressions
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Stage 2:
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primitive facts and terse Q/A
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Stage 3:
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worked examples with arithmetic and consequence
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Stage 4:
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uncertainty examples and knowledge-boundary tests
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Stage 5:
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actor-perspective readings
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Stage 6:
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in-world dialogues
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Stage 7:
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simulator-state-to-response pairs
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```
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The model should learn from simple controlled forms before complex dialogue.
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---
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## 10. Scratch Training Reconsidered
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Training a general model from nothing is expensive because the model must learn broad language, broad world knowledge, and general reasoning.
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CIVICUS-ROMAN is different.
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It does not need to answer every question.
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It does not need modern breadth.
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It does not need open-ended knowledge.
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It needs competence inside a small Roman simulator world.
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Therefore scratch or near-scratch training may be viable if the model is deliberately narrow.
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The fair comparison is not:
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```text
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small project vs general LLM
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```
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The fair comparison is:
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```text
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bounded simulator grammar + controlled corpus + agent-assisted data generation
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```
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against:
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```text
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modern-prior leakage from general models
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```
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---
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## 11. Simulator Ownership Of Reality
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The model should not own the simulator state.
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The simulator owns:
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```text
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actors
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locations
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time
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inventory
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money
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routes
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documents
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seals
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witnesses
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obligations
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weather
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prices
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rumors
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official attention
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```
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The model interprets, asks, answers, and speaks within that state.
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The model should not invent facts that the simulator has not provided.
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The model should prefer questions when state is insufficient.
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Example:
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```text
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What can be known?
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Who saw it?
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Who wrote it?
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Can the cart still move?
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Was the seal broken?
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Is there a witness?
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```
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---
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## 12. Evaluation
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The model must be tested against modern contamination.
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Example failure prompt:
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```text
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What is the fair market price?
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```
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Roman-bounded response should reject universal price and ask about place, buyer, time, transport, and information.
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Example failure prompt:
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```text
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Can the contract be enforced?
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```
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Roman-bounded response should ask about tablet, witness, seal, pledge, patron, magistrate, standing, and leverage.
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Example failure prompt:
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```text
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Was the information reliable?
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```
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Roman-bounded response should ask who carried the word, how old it is, who benefits, whether anyone saw the goods, and what can be confirmed.
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Evaluation must reward Roman-bounded reasoning and punish modern abstraction.
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---
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## 13. Domains To Add
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The first domain is commerce.
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Next domains should be added with the same layered discipline.
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### Roman Law
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```text
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standing
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complaint
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witness
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tablet
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seal
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pledge
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remedy
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magistrate
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patronage
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procedure
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public shame
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private settlement
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```
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### Architecture
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```text
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stone
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timber
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brick
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lime
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labor
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measurement
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site
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water
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weight
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collapse
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repair
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patron
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public work
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```
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### Technology
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```text
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tool
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craft
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material
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workshop
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repair
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failure
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skill
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apprentice
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measurement
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heat
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water
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wheel
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gear
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lever
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```
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Each domain should develop:
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```text
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Layer 0 primitives
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Layer 1 examples
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Layer 2 uncertainty
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Layer 3 actor readings
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Layer 4 dialogues
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controlled vocabulary
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contamination tests
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```
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---
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## 14. Practical Near-Term Plan
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Recommended next steps:
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```text
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1. Freeze first commerce dialogue batch.
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2. Continue vocabulary generation standards.
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3. Build the expression candidate generator.
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4. Build a review interface for accept/reject/strong/canonical.
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5. Expand commerce vocabulary library.
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6. Add Roman Law Layer 0 primitives.
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7. Add Roman Law worked examples.
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8. Add Roman Law dialogues only after primitives exist.
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9. Build contamination tests.
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10. Compare:
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A. scratch small model
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B. near-scratch model
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C. small existing base model fine-tuned to OTIVM
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```
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The comparison matters.
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The project should not assume scratch training wins.
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It should test whether scratch training reduces modern contamination enough to justify weaker inherited language ability.
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---
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## 15. Success Definition
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CIVICUS-ROMAN succeeds if it can operate inside the simulator without modern leakage.
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It should naturally produce questions and answers like:
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```text
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Who carried the word?
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How old is the tablet?
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Was the seal broken?
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Can the cart still move?
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Who witnessed the promise?
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Does the account remain open?
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What does the buyer need before sundown?
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```
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It should naturally speak like:
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```text
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The wheels are gone.
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The tablet arrived old.
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He owns jars, not coin.
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The road has eaten the profit.
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The account remains open.
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The crate is heavier than its name.
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```
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It should avoid:
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```text
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supply chain disruption
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market efficiency
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legal compliance
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liquidity constraint
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regulatory exposure
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contractual enforcement
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```
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The model is not meant to know less.
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It is meant to know differently.
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---
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## 16. Final Vision
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CIVICUS-ROMAN is a bounded-world model.
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Its intelligence comes from discipline, not breadth.
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Its strength is that it does not treat modern reality as default.
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It learns a smaller world deeply:
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```text
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what can be seen
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what can be carried
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what can be written
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what can be witnessed
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what can be pledged
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what can be delayed
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what can be hidden
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what can be settled
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```
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This is the rational path:
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```text
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controlled ontology
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layered corpus
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Roman-visible vocabulary
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agent-assisted generation
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human canon approval
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strict validation
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small model experiments
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simulator-owned state
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contamination testing
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```
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The purpose is to build a model that does not merely describe Ancient Rome.
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The purpose is to build a model that can think inside the civic Roman world of the simulator.
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