Distributed Cognitive Network
Volume V
DCN Runtime Specification 0.1
Part 3B
SurrealDB World Model, Memory Architecture, Context Management, and Cognitive State
108. Purpose
This specification defines the persistent cognitive substrate of a Mind.
The world model is not merely storage.
It is the continuously evolving representation of:
- reality;
- goals;
- cognition;
- relationships;
- authority;
- memory;
- K-lines;
- active work.
All cognition executes against this shared substrate.
109. Design Philosophy
Traditional assistants store:
chat history
embeddings
documents
RAG
DCN Runtime stores:
an evolving semantic world model.
The world model is authoritative.
Conversations are merely one source of observations.
110. World Model
The world model answers:
What does the Mind currently believe about reality?
It includes uncertainty.
It includes contradictions.
It includes provenance.
It is never assumed complete.
111. World State
The world state consists of:
entities
relationships
events
goals
tasks
beliefs
observations
constraints
authority
active cognition
The runtime reasons over world state rather than documents.
112. SurrealDB as Cognitive Substrate
SurrealDB provides:
- graph relationships;
- documents;
- events;
- queries;
- transactions;
- versioning.
DCN Runtime adds cognitive semantics.
SurrealDB is therefore the storage engine, not the cognitive model.
113. Memory Layers
The architecture distinguishes several memory systems.
Working Memory
↓
Context Memory
↓
Episodic Memory
↓
Semantic Memory
↓
Procedural Memory
↓
World Model
These are distinct.
114. Working Memory
Working memory contains:
active task
active experts
temporary variables
intermediate reasoning
pending patches
current observations
Working memory is intentionally small.
It is discarded when no longer needed.
115. Context Memory
Context memory answers:
What matters right now?
Examples:
current meeting
current location
current project
current patient
current conversation
current deadlines
Context changes rapidly.
116. Episodic Memory
Episodes record:
what happened.
Examples:
meeting
conversation
diagnosis
trip
execution
evaluation
Episodes are immutable historical records.
117. Semantic Memory
Semantic memory stores:
facts
concepts
relationships
definitions
preferences
long-term knowledge
Semantic memory evolves more slowly.
118. Procedural Memory
Procedural memory contains:
K-lines
artifacts
skills
compiled cognition
This is where cognition lives.
119. World Model
The world model integrates:
semantic memory
episodic memory
procedural memory
active goals
active plans
relationships
into one coherent graph.
120. Core Object Classes
Recommended object types:
Person
Organization
Device
Agent
Expert
Artifact
K-line
Goal
Task
Project
Conversation
Observation
Evidence
Evaluation
Policy
Authority
Relationship
Applications extend these.
121. Relationship First
Objects matter less than relationships.
Examples:
OWNS
WORKS_ON
TRUSTS
CREATED
PROMOTED
EVALUATED
SUPERSEDES
BELONGS_TO
USES
Graph traversal becomes natural cognition.
122. Beliefs
The world model stores beliefs.
Not truths.
Example:
Object
↓
Belief
↓
Confidence
↓
Evidence
Beliefs evolve.
123. Contradictions
Contradictory beliefs are permitted.
Example:
Source A
↓
Blood pressure
140
Source B
↓
Blood pressure
128
The runtime resolves contradictions.
The database preserves them.
124. Provenance
Every belief SHOULD reference:
source
timestamp
evidence
confidence
Knowledge without provenance is discouraged.
125. Confidence
Confidence belongs to beliefs.
Example:
Patient smokes
confidence
0.94
Confidence changes over time.
126. Trust
Trust belongs to sources.
Not beliefs.
Examples:
hospital
research paper
human
expert
organization
sensor
Trust is local.
127. Goals
Goals are persistent objects.
Schema:
Goal
priority
deadline
constraints
dependencies
progress
owner
Goals participate in the world graph.
128. Tasks
Tasks differ from goals.
Goal:
desired future.
Task:
specific work.
Tasks may satisfy goals.
129. Projects
Projects group:
- goals;
- tasks;
- conversations;
- artifacts;
- experts.
Projects provide long-term organization.
130. Plans
Plans are executable strategies.
A plan references:
goals
tasks
K-lines
experts
constraints
Plans evolve.
131. Active Plans
Only a small number remain active.
Inactive plans stay historical.
132. Observations
Everything begins as:
Observation
Examples:
email
voice
camera
sensor
calendar
location
health
Interpretation follows later.
133. Observation Pipeline
Observe
↓
Interpret
↓
Belief
↓
Episode
↓
Learning
Observation is never skipped.
134. Context Windows
Unlike LLM context,
DCN Runtime context is semantic.
Context is assembled from:
goals
world state
active plans
working memory
relevant episodes
K-lines
authority
Not merely token history.
135. Context Assembly
Context assembly becomes a runtime algorithm.
Inputs:
problem
goal
entities
relationships
time
location
authority
Output:
Working Context
136. Context Size
The runtime should minimize unnecessary context.
Smaller context means:
- faster reasoning;
- lower cost;
- higher locality.
137. Context Lifetime
Different context expires differently.
Example:
meeting
↓
hours
project
↓
months
identity
↓
years
138. Attention Anchors
Working memory maintains:
current focus
secondary focus
background focus
Attention shifts dynamically.
139. Long-Term Forgetting
The system SHOULD forget selectively.
Not everything deserves permanent storage.
Possible policies:
discard
archive
compress
summarize
compile
140. Compression
Repeated episodes MAY become:
summary
statistics
K-line
artifact
Memory itself evolves.
141. Semantic Compression
Instead of storing:
1000 conversations
store:
relationship
preference
goal
trust
The runtime preserves meaning.
142. Cognitive Compression
Repeated reasoning MAY become:
K-line
↓
artifact
This mirrors memory evolution.
143. Time
Every object possesses:
created
observed
updated
expires
revalidated
Time is fundamental.
144. Space
Location is first-class.
Examples:
device
building
city
country
virtual workspace
Context often depends upon location.
145. Identity
Identity links:
person
devices
accounts
credentials
authorities
Identity remains external to cognition.
146. Authority Objects
Authority is represented explicitly.
Examples:
delegation
policy
approval
consent
Authority is queried.
Never assumed.
147. World Events
Events mutate belief.
Not directly.
Pipeline:
event
↓
observation
↓
interpretation
↓
patch
↓
commit
148. World Consistency
Consistency is maintained by:
- patch validation;
- authority;
- conflict resolution.
Not by Agencies.
149. History
History remains immutable.
Current world state is reconstructed through accepted patches.
150. Snapshots
The runtime MAY periodically checkpoint:
world state
working memory
goals
plans
Snapshots accelerate recovery.
151. Search
Search is semantic.
Typical queries:
What do I know?
Who knows?
What happened?
Have I solved this?
Who evaluated this?
Which artifact replaced this?
Graph traversal dominates keyword search.
152. Memory Promotion
Interesting memories MAY become:
belief
↓
episode
↓
K-line
↓
artifact
Memory itself participates in learning.
153. Local First
Everything personal remains local whenever possible.
Federation is selective.
Not default.
154. Synchronization
Synchronization SHOULD exchange:
Acts
patches
artifacts
K-lines
evaluations
rather than entire databases.
155. Runtime Invariants
The world model SHALL preserve:
-
Shared authoritative state.
-
Explicit provenance.
-
Explicit confidence.
-
Explicit trust.
-
Graph relationships.
-
Immutable history.
-
Proposal-based mutation.
-
Local autonomy.
156. Strategic Observation
Traditional AI systems remember conversations.
DCN Runtime remembers the world.
Conversations become merely one stream of observations feeding a persistent cognitive substrate.
That distinction transforms memory from passive storage into an active semantic environment in which cognition can accumulate, evolve, and continuously improve over the lifetime of the Mind.
This is, in my view, one of the biggest architectural departures from current AI assistants. Instead of centering everything on the LLM's context window, DCN Runtime centers everything on a persistent semantic world model. The LLM—or any other expert—becomes just one transient reasoning component operating over that world, while SurrealDB and the Cognitive Kernel maintain continuity across years of interaction. That shift is what makes long-lived, continually learning Minds possible.