Distributed Cognitive Network

Volume IV

Actum Verifiable Cognition Profile 0.1

Part 3B

PromotionAct, CompilationAct and ArtifactPublicationAct

117. Purpose

Execution creates history.

Evaluation measures history.

Promotion transforms measured cognition into reusable cognition.

Compilation transforms reusable cognition into executable cognitive capital.

Publication makes that capital discoverable.

These three Acts therefore define the mechanism by which the network continually converts expensive System-2 reasoning into abundant System-1 capability.

118. Promotion Philosophy

Promotion is not:

"this K-line is correct."

Promotion means:

"according to a defined policy, there is now sufficient evidence that this KLineTemplate may participate in System 1."

Promotion is therefore:

contextual;
reversible;
historical;
policy-driven.

119. Promotion Does Not Create Truth

Promotion SHALL NOT imply:

universal correctness;
permanent validity;
universal trust;
mandatory activation.

Promotion only records:

this cognitive organization satisfied this promotion policy at this point in history.

120. Promotion Target

Promotion SHALL reference:

KLineTemplate

not:

Episode

Artifact

Episodes remain evidence.

Artifacts remain implementations.

The Template is what becomes eligible for System 1.

121. Promotion Preconditions

Promotion MAY require:

minimum episode count

minimum evaluation count

minimum evaluator independence

minimum quality

maximum failure rate

minimum coverage

minimum calibration

authority approval

absence of unresolved anomalies

Policies define thresholds.

122. Promotion Policy

Promotion SHALL reference an explicit PromotionPolicy.

Example:

{
  "policy":"promotion:v1",

  "minimum_episodes":50,

  "minimum_independent_evaluations":3,

  "minimum_quality":0.95,

  "maximum_failure_rate":0.02
}

The policy itself becomes historically referencable.

123. PromotionAct Schema

{
  "schema":"actum.promotion.v1",

  "act_id":"...",

  "template":{

      "id":"kline:template:...",

      "hash":"sha256:..."
  },

  "promotion_policy":"promotion:v1",

  "supporting_evaluations":[

  ],

  "supporting_episodes":[

  ],

  "prior_state":{

      "status":"validated"
  },

  "result_state":{

      "status":"promoted"
  },

  "authority":{},

  "evidence_graph":{},

  "assurance":{},

  "created_at":"..."
}

124. Promotion State Machine

Recommended lifecycle:

candidate

↓

validated

↓

promoted

↓

active

↓

degraded

↓

revalidation

↓

active

degraded

↓

superseded

Promotion never bypasses evaluation.

125. Promotion Lineage

Promotion SHALL preserve lineage.

Graph:

Episodes

↓

Evaluations

↓

Promotion

↓

Template

Promotion never disconnects evidence.

126. Why Promotion Exists

Without Promotion,

System 1 becomes:

"whatever happened last time."

Promotion formalizes:

repeated success has become reusable cognition.

127. Compilation

Promotion says:

this cognition may be reused.

Compilation asks:

how can this cognition execute more efficiently?

Compilation is therefore optimization.

128. Compilation Philosophy

Compilation SHALL preserve semantics.

It MAY change implementation.

Examples:

frontier model

↓

small model

five experts

↓

two experts

LLM

↓

rules

provided required quality remains.

129. Two Compilers

Version 1 recognizes:

TopologyCompiler

ArtifactCompiler

These remain conceptually distinct.

130. TopologyCompiler

Input:

Template

Episodes

Evaluations

Output:

simpler topology

Example:

7 experts

↓

4 experts

↓

2 experts

No implementation change required.

Only topology changes.

131. ArtifactCompiler

Input:

Template

Chosen topology

Episodes

Output:

Executable artifact

Examples:

fine-tuned model

classifier

mobile runtime

workflow

graph executor

symbolic engine

132. Compilation Objectives

Compilation SHALL optimize:

cost

latency

energy

memory

network

quality

Typical optimization:

[ \min(C+\lambda L+\mu E+\nu M) ]

subject to:

[ Q\ge Q_{min} ]

133. Compilation Record

Compilation SHALL preserve:

source template

compiler

episodes

constraints

objective

artifact

134. CompilationAct Schema

{
"schema":"actum.compilation.v1",

"template":{},

"compiler":{},

"episodes":[ ],

"evaluations":[ ],

"optimization":{},

"artifact":{},

"authority":{},

"evidence_graph":{},

"created_at":"..."
}

135. Compiler Identity

Compiler SHOULD be explicit.

Example:

Cognitive Compiler

Organization Compiler

Human Compiler

Research Compiler

Compilation itself becomes attributable.

136. Compilation Constraints

Examples:

iPhone only

offline

private

medical

battery optimized

GPU available

Constraints affect produced artifacts.

137. Artifact

Compilation creates:

Artifact

Artifacts are executable.

Templates are semantic.

Episodes are historical.

This distinction is fundamental.

138. Artifact Identity

Artifact SHALL possess:

ArtifactID

ContentHash

SemanticHash

and reference:

Template

Compilation

139. Artifact Types

Version 1:

workflow

graph

small model

local model

mobile runtime

classifier

rules

hybrid

Extensions expected.

140. Artifact Metadata

Artifacts SHOULD expose:

hardware

memory

energy

latency

privacy

dependencies

runtime

size

These become routing inputs.

141. Artifact Evaluation

Artifacts SHALL be independently evaluated.

Compilation does not imply quality.

Artifacts therefore accumulate their own EvaluationActs.

142. Artifact Lifecycle

Recommended:

candidate

↓

evaluated

↓

published

↓

active

↓

degraded

↓

superseded

143. Publication

Compilation creates an artifact.

Publication makes it discoverable.

These are different Acts.

144. Publication Philosophy

Publishing SHOULD NOT imply:

activation.

Publication merely means:

discoverable.

145. ArtifactPublicationAct

Schema:

{
"schema":"actum.publication.v1",

"artifact":{},

"visibility":{

"public":true

},

"license":{},

"publisher":"...",

"metadata":{},

"created_at":"..."
}

146. Visibility

Version 1:

private

organization

federation

public

147. Discovery

Publication enables:

search

download

license

purchase

evaluation

through Actum Compute.

148. Publication Does Not Grant Trust

Published artifacts remain:

untrusted

until accepted locally.

Publication never bypasses Kline trust.

149. Artifact Market

Published artifacts become:

cognitive capital

They may be:

free

licensed

subscription

royalty

private

The protocol remains neutral.

150. Cognitive Capital

An artifact is:

executable cognition.

It therefore represents accumulated cognitive investment.

Unlike model calls,

artifacts become durable assets.

151. Lineage

Publication SHALL preserve:

Template

↓

Episodes

↓

Evaluations

↓

Promotion

↓

Compilation

↓

Artifact

↓

Publication

Nothing is lost.

152. Recompilation

Artifacts MAY be recompiled repeatedly.

Example:

GPU artifact

↓

mobile artifact

↓

watch artifact

All preserve lineage.

153. Compiler Competition

Different compilers MAY produce different artifacts.

Competition occurs through:

Evaluation,

not authority.

154. Promotion Is Historical

Compilation Is Creative

Publication Is Economic

These three Acts deliberately separate:

learning,

optimization,

distribution.

Keeping them independent greatly simplifies governance.

155. Promotion and Compilation

Promotion references:

Template

Compilation references:

Template

+

Episodes

Artifacts therefore inherit:

knowledge,

not authority.

156. Strategic Observation

This is the mechanism by which intelligence becomes infrastructure.

Experts perform cognition once.

The network:

evaluates,

promotes,

compiles,

publishes,

and reuses that cognition millions of times.

That transformation—from transient reasoning into durable, reusable, executable cognitive capital—is the defining innovation of the architecture.

The PromotionAct records when a cognitive organization became eligible for reuse.

The CompilationAct records how that cognition was transformed into an efficient implementation.

The ArtifactPublicationAct records when that implementation entered the wider ecosystem as shareable cognitive capital.

Together, these three Acts define the lifecycle by which the Distributed Cognitive Network continuously expands its System-1 coverage while preserving complete provenance, evaluation history, and lineage back to the original System-2 discoveries.

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