Distributed Cognitive Network

Volume I — Vision, Architecture, and System Boundaries

Draft Specification 0.1

1. Purpose

This specification defines a distributed cognitive architecture in which intelligence is produced not by a single monolithic model, but by a society of specialized experts, reusable cognitive structures, local personal context, verifiable external execution, and a continuously learning routing system.

The central thesis is:

The scarce resource is not tokens. It is proven problem-solving capability.

Individual frontier models, specialist models, tools, humans, deterministic programs, K-lines, and compiled cognitive artifacts are treated as experts or capabilities within a larger cognitive network.

The system discovers which combinations of capabilities successfully solve classes of problems, records those executions as episodes, evaluates them, promotes successful patterns into reusable K-lines, and progressively compiles expensive System-2 reasoning into cheaper System-1 cognition.

The objective is therefore not merely to increase model capability.

The objective is to continuously transform:

scarce intelligence → validated cognition → reusable cognitive capital → efficient local execution.

2. Terminology

2.1 Distributed Cognitive Network

The Distributed Cognitive Network, abbreviated DCN, is the complete network of:

• personal Minds;
• K-lines;
• experts;
• cognitive artifacts;
• evaluation services;
• Actum Compute markets;
• evidence providers;
• execution environments;
• capability registries;
• shared cognitive knowledge.

The DCN is federated.

There is no single canonical global brain and no requirement that every participant trust the same experts, evaluations, or K-lines.

Each Mind maintains its own local trust view and determines what cognition it will activate.

Architectural invariant:

The network shares cognition. The local Mind decides what becomes active.

2.2 Mind

A Mind is a persistent personal or organizational cognitive runtime.

A Mind owns:

• world state;
• memory;
• goals;
• active plans;
• personal K-lines;
• cached external K-lines;
• expert reputation;
• trust policies;
• authority context;
• local cognitive artifacts;
• current execution state.

A Mind is not equivalent to an LLM.

An LLM is one possible expert used by a Mind.

A Mind may operate even when no frontier LLM is active.

2.3 Expert

An Expert is any capability-producing entity that can participate in cognition.

Examples include:

• a frontier coding model;
• a medical specialist model;
• a local fine-tuned model;
• a retrieval agent;
• a theorem prover;
• a symbolic reasoning engine;
• a deterministic program;
• an MCP capability;
• another Mind;
• a human expert;
• a K-line artifact.

Experts are described primarily by capability rather than vendor identity.

Example:

capability:
    software_engineering

quality_floor:
    0.94

evidence:
    provider_authenticated

privacy:
    remote_allowed

At execution time this capability could resolve to Codex, Claude, a local model, a human engineer, or a previously compiled K-line artifact.

3. Core Architectural Thesis

Today's dominant AI architecture can be represented approximately as:

problem
  ↓
large model
  ↓
answer

The DCN replaces this with:

problem
   ↓
Coordinator
   ↓
known K-line?
 /            \
yes            no
 ↓              ↓
System 1     System 2
 ↓              ↓
compiled      discover/
cognition     compose experts
 \              /
  \            /
      execute
         ↓
      evaluate
         ↓
       learn
         ↓
     K-line graph

The intelligence of the system resides increasingly in:

• selecting appropriate cognition;
• activating appropriate experts;
• remembering successful cognitive topology;
• validating outcomes;
• compiling repeated deliberation;
• recognizing novelty;
• knowing when previous cognition is no longer appropriate.

4. System 1 and System 2

The architecture explicitly adopts a dual-process model.

4.1 System 1

System 1 contains cognition that is already known, validated, and sufficiently cheap to invoke routinely.

Examples:

• deterministic rules;
• learned reflexes;
• compiled K-line artifacts;
• stable expert workflows;
• local classifiers;
• cached procedural structures;
• well-established K-lines.

System 1 optimizes primarily for:

• latency;
• cost;
• locality;
• energy use;
• predictability.

System 1 must never be assumed permanently correct.

Its cognition is provisional and subject to monitoring, decay, revalidation, supersession, and reopening by System 2.

4.2 System 2

System 2 handles novelty, uncertainty, disagreement, insufficient precedent, and difficult cognition.

System 2 may:

• recruit several experts;
• search existing K-lines;
• combine K-lines;
• explore alternative expert topologies;
• critique proposed solutions;
• perform external retrieval;
• invoke frontier models;
• request human review;
• conduct simulations;
• evaluate competing hypotheses;
• test solutions.

System 2 is expensive by design.

The purpose of System 2 is not merely to solve the current problem.

Successful System-2 trajectories are candidates for becoming future System-1 cognition.

5. K-lines

K-lines are the central learned cognitive structure.

A K-line is not simply:

• a prompt;
• a conversation;
• a vector embedding;
• an LLM trace;
• a static workflow.

A K-line represents a reusable organization of cognition that has been learned to address a class of situations.

The normative architecture distinguishes three related objects.

5.1 KLineTemplate

The template defines what the cognition means.

It contains:

KLineTemplate
├── trigger semantics
├── preconditions
├── activation graph
├── data flow
├── control flow
├── capability requirements
├── context requirements
├── authority requirements
├── validation strategy
├── budget policy
├── evidence requirements
├── fallbacks
├── expected outputs
└── lifecycle metadata

A template should normally contain capability requirements rather than fixed commercial vendors.

Example:

independent_critic:
    capability: independent_reasoning
    minimum_quality: 0.93
    required_evidence: verified_execution

not:

independent_critic:
    model: VendorModelX

5.2 KLineEpisode

An episode records what actually happened during one execution.

Example:

Template:
    inherited_cardiomyopathy_workup

Bindings:
    reasoning → Expert A
    evidence → Expert B
    genetics → Model C
    critic → Expert D

Evidence:
    TLSNotary receipt P
    attestation Q

Outcome:
    accepted

Evaluation:
    Protocol E17

Cost:
    €3.82

Latency:
    28s

The episode provides provenance and empirical evidence for whether the template works.

5.3 KLineArtifact

An artifact is a compiled executable implementation of a K-line.

The same KLineTemplate may have many artifacts:

Artifact A
4 remote frontier experts
€7.40 / execution

Artifact B
2 experts + deterministic workflow
€1.10 / execution

Artifact C
local specialist model
€0.04 / execution

Artifact D
mobile classifier + rules
≈ €0

Artifacts may vary by:

• hardware;
• jurisdiction;
• trust requirements;
• execution environment;
• cost;
• energy use;
• latency;
• privacy;
• quality.

The semantic K-line may remain stable while artifacts evolve.

6. The Cognitive Compiler

The Cognitive Compiler is a first-class subsystem implemented as a Compiler Society of specialized compilation agencies.

Its purpose is to transform expensive successful cognition into progressively cheaper implementations while preserving required quality.

The lifecycle is:

DISCOVER
   ↓
REIFY
   ↓
VALIDATE
   ↓
PROMOTE
   ↓
COMPILE
   ↓
DEPLOY
   ↓
MONITOR
   ↓
DECAY
   ↓
REOPEN

The compiler performs two distinct forms of optimization.

6.1 Topological Compilation

Reduce unnecessary cognition.

Example:

7 experts
   ↓
5 experts
   ↓
3 experts
   ↓
deterministic routing + 2 experts

The objective is to identify which cognitive interactions are actually necessary.

6.2 Artifact Compilation

Replace expensive implementations with cheaper equivalents.

Example:

remote frontier experts
        ↓
small fine-tuned model
        ↓
local classifier
        ↓
rules/reflex

A simplified optimization objective is:

[ \min_{\pi} C(\pi) +\lambda L(\pi) +\mu E(\pi) ]

subject to:

[ Q(\pi)\ge Q_{min} ]

where:

• (C) = monetary cost;
• (L) = latency;
• (E) = energy consumption;
• (Q) = measured quality.

Energy is explicitly included because cognition should eventually be compilable onto constrained devices such as phones.

7. Cognitive Coverage

Progress should not be measured solely by benchmark scores or model size.

The network seeks to increase cognitive coverage.

For distribution (D):

[ Coverage_{S1}(D)

P_{q\sim D} [ \exists K: K(q) \text{ satisfies required quality using System 1} ] ]

and:

[ Coverage_{Total}(D)

P_{q\sim D} [ \text{the Mind can solve }q ] ]

The difference:

[ Frontier(D)

Coverage_{Total}(D)

Coverage_{S1}(D) ]

represents approximately the region that System 2 can solve but has not yet compiled.

Important system metrics include:

System-1 coverage
Total solve coverage
Frontier size
Novelty rate
System-2 escalation rate
Compilation ratio
Compilation gain
Cost per solved task
Energy per solved task
Artifact locality
K-line reuse
K-line half-life
Failure surprise
Revalidation rate

This creates an empirical framework for measuring the growth of the cognitive network.

8. Cognitive Kernel

The Cognitive Kernel is the Mind's cognitive allocation system.

It should not be designed as another all-knowing LLM.

Its fundamental question is:

What cognition should become active now?

Inputs include:

problem
world state
personal context
known K-lines
available artifacts
available experts
budgets
authority
risk
privacy
device state
novelty
trust

The Cognitive Kernel performs:

trigger matching
K-line retrieval
capability resolution
trust filtering
novelty detection
budget allocation
System-1/System-2 routing

A powerful frontier model may participate when necessary, but routing should increasingly be achievable through cheap models, graph operations, retrieval, deterministic logic, and learned policies.

9. Trust and Admissibility

Trust is not merely another positive term in a utility function.

Candidates that do not satisfy required trust, authority, privacy, evidence, or policy constraints should be excluded before optimization.

Define:

[ \Pi_

{ \pi: Policy(\pi,c)=allow \land Trust(\pi,c)\ge T_{min} \land Evidence(\pi)\supseteq E_{required} } ]

Only then select:

[ \pi^*

\arg\max_{\pi\in\Pi_{admissible}} \left[ Q -\lambda C -\mu L -\rho R -\eta E \right] ]

This establishes a fundamental architectural rule:

Money, speed, or quality cannot compensate for missing authority, evidence, or minimum trust.

10. Evaluation

No K-line, artifact, or expert should possess an unqualified score such as:

success_rate = 97%

Evaluation is always contextual.

10.1 EvaluationProtocol

An EvaluationProtocol defines:

EvaluationProtocol
├── dataset/distribution commitment
├── sample policy
├── scorer
├── metrics
├── contamination policy
├── environment
├── evaluator requirements
└── acceptance thresholds

10.2 EvaluationClaim

An immutable EvaluationClaim contains:

EvaluationClaim
├── subject
├── protocol
├── distribution
├── environment
├── result
├── evidence
├── evaluator
└── timestamp

Therefore a valid statement is:

Artifact A implementing Template K achieved 97.1% under Protocol P on Distribution D in Environment E.

11. Decay, Drift, and Reopening

System-1 cognition must never become immortal precedent.

K-lines and artifacts should support:

validity_window
last_evaluated
environment_signature
distribution_signature
confidence_decay
requires_revalidation
superseded_by
revoked

Possible lifecycle:

candidate
   ↓
validated
   ↓
promoted
   ↓
active
   ↓
degraded
  /   |   \
 /    |    \
revalidate supersede revoke
   |
 active

System 2 must always retain the authority to challenge existing cognition.

A novelty or surprise signal:

[ N(q,c,K) ]

should trigger System-2 escalation when known K-lines fit poorly or produce unexpected outcomes.

The system must be capable of concluding:

Existing precedent should not be trusted for this case.

12. Federation

There is no globally authoritative K-line database.

K-lines are globally shareable, not globally mandatory.

A Mind may discover:

Template K from organization A
Template K' from researcher B
Template K'' from community C

and independently decide:

trusted
experimental
cached
deprecated
blocked

This protects against:

• poisoning;
• Sybil manipulation;
• benchmark gaming;
• stale cognition;
• correlated failures;
• malicious compositions;
• popularity bias.

13. Identity and Lineage

Three identities are distinguished.

ArtifactHash

Exact cryptographic content identity.

TemplateID

Immutable authored object identity.

SemanticFingerprint

Similarity representation used to discover semantically related K-lines.

This supports:

K123
├── K123-A
├── K123-B
└── K123-C

without pretending that semantic similarity means cryptographic identity.

14. Kline and SurrealDB

Kline is the cognitive layer.

SurrealDB is the primary local state substrate for the Mind.

Conceptually:

SurrealDB
├── world state
├── episodic memory
├── goals
├── active plans
├── KLineTemplates
├── KLineEpisodes
├── KLineArtifacts
├── expert observations
├── evaluation claims
├── trust views
├── capability cache
└── authority references

The conceptual hierarchy is:

WORLD STATE
what is believed to be true

EPISODIC MEMORY
what happened

K-LINE
how the Mind knows how to think or act
about a class of situations

ARTIFACT
how that cognition is efficiently
implemented today

K-lines are therefore best understood as procedural cognition rather than ordinary memory. Evaluated K-lines, artifacts, episodes, evidence, and lineage collectively contribute to the Mind's Cognitive Capital.

15. Actum Compute

Actum Compute is the economic and capability-resolution layer beneath Kline.

It must not become the Cognitive Kernel.

Its job is to answer:

Given a CapabilityRequirement, which admissible suppliers can provide it, with what evidence, quality, price, latency, and privacy properties?

Actum Compute supports markets for:

expert execution
specialist models
frontier-model execution
raw compute
K-line artifacts
evaluation
verification/evidence
potentially human expertise

16. CapabilityRequirement

The principal interface between Kline and Actum Compute is a capability request.

Conceptually:

CapabilityRequirement
├── capability
├── input schema
├── output schema
├── quality floor
├── required evidence
├── trust policy
├── privacy policy
├── jurisdiction policy
├── maximum latency
├── maximum cost
├── energy preference
├── execution-location policy
└── fallback semantics

Actum Compute returns one or more candidate resolutions.

Kline retains the final routing decision.

Architectural invariant:

Actum Compute discovers and prices admissible cognition. Kline decides what cognition to activate.

17. Expert and Artifact Markets

Actum Compute should support at least two major supply classes.

ExpertOffer

An offer to execute cognition.

Example:

Expert:
    frontier coding expert

Capabilities:
    software_engineering
    debugging
    code_review

Evidence:
    TLSNotary

Price:
    €2.40

Execution:
    remote

ArtifactOffer

An offer to license or execute compiled cognition.

Example:

Artifact:
    KLineArtifact A17

Template:
    inherited_cardiomyopathy_workup

Target:
    iPhone / Apple Silicon

Quality:
    EvaluationClaim E51

Price:
    €20 licence
    €0.001 execution royalty

This allows cognitive capital itself to become economically valuable.

18. Actum

Actum is the verifiable action and finality layer.

Actum should record durable claims and state transitions, including:

ExecutionAct
SettlementAct
EvaluationAct
PromotionAct
SupersessionAct
ArtifactPublicationAct
KLineEpisodeAct

Actum provides:

• identity;
• commitments;
• provenance;
• lineage;
• evidence references;
• authorization records;
• finality;
• settlement state;
• economic transfer.

Actum does not magically establish truth.

It establishes that:

• a claim was made;
• by a particular principal;
• under particular authority;
• with particular evidence;
• at a particular point in the network's finalized history.

Architectural invariant:

Provenance is not truth.

Kline decides how much local trust to assign to evidence recorded on Actum.

19. Actum Compute and Actum

Actum Compute performs economic coordination.

Actum supplies the verifiable state substrate underneath it.

Typical execution:

Kline
   ↓
CapabilityRequirement
   ↓
Actum Compute
   ↓
ExpertOffer matched
   ↓
JobAssignment
   ↓
expert execution
   ↓
ExecutionEvidence
   ↓
Actum ExecutionAct
   ↓
verified settlement condition
   ↓
Actum SettlementAct

20. TLSNotary and Distill

Remote frontier-model execution creates a trust problem.

A supplier could claim:

This task was executed by the current flagship model.

while actually returning output from:

• a cheaper model;
• a local model;
• a cached answer;
• a dummy response.

TLSNotary provides evidence about authenticated external TLS interactions.

For supported execution classes, the evidence layer should bind:

provider endpoint
request commitment
model identifier when authenticated/disclosed
response commitment
execution challenge
freshness
TLS transcript commitment
notary/verifier identity

This makes model identity part of an execution assurance class rather than an unverifiable marketing claim.

TLSNotary does not prove hidden server internals or model weights.

It proves authenticated communication and selectively disclosed values from that communication.

Distill-derived infrastructure should be reused for:

• challenge binding;
• transcript ingestion;
• provider adapters;
• canonical hashing;
• replay nullifiers;
• signed receipts;
• selective disclosure processing.

21. ZeroK

ZeroK is the confidential verification layer.

It allows claims to be proven without revealing unnecessary underlying data.

Potential predicates include:

required provider class satisfied

required model class satisfied

execution receipt valid

price <= authorized maximum

seller entitled to settlement

evaluation score >= threshold

artifact evaluated on >= N cases

failure rate < threshold

same execution not previously settled

ZeroK may protect:

• prompts;
• responses;
• buyer identity;
• seller identity;
• exact provider identity;
• commercial pricing;
• evaluation data;
• reputation history.

Actum can therefore verify the relevant state transition without requiring all sensitive data to become public.

22. Agent Plugins

Agent Plugins provide the primary distribution mechanism for network capabilities.

The plugin layer packages:

skills
+
MCP tools

A plugin may represent:

• an expert;
• Actum Compute;
• Kline federation;
• an evaluator;
• an artifact;
• a donor execution worker;
• a specialist capability.

Agent Plugins are a distribution and interface layer.

They do not themselves define:

• authority;
• trust;
• sandboxing;
• settlement;
• cognitive promotion.

Those responsibilities remain in the appropriate underlying layers.

23. MCP and A2A

MCP is used primarily for tool and capability invocation.

A2A is used for communication between autonomous agents or Minds.

Conceptually:

MCP:
    invoke this capability

A2A:
    negotiate/communicate with this agent

Kline may discover both through the wider capability fabric.

24. Society of Experts

The network should be understood as a Society of Experts.

A problem may activate:

frontier reasoning model
+
local specialist
+
retrieval expert
+
symbolic verifier
+
K-line artifact
+
human reviewer

No single expert needs to possess general intelligence.

The system's capability emerges from:

• expert specialization;
• routing;
• composition;
• memory of successful organization;
• evaluation;
• compilation.

A K-line therefore captures not merely an answer but:

which kinds of cognition should become active together for this class of problem.

25. Economic Flywheel

The network's economic mechanism is:

novel problem
      ↓
scarce cognition required
      ↓
experts earn money
      ↓
successful System-2 episode
      ↓
K-line discovered
      ↓
evaluated
      ↓
promoted
      ↓
Cognitive Compiler
      ↓
cheap artifact
      ↓
massive reuse
      ↓
scarce expert attention moves outward
      ↓
next frontier

The network continuously converts:

scarce intelligence into abundant cognition.

This is the central economic thesis.

26. Cognitive Capital

The system recognizes several economically valuable assets.

Expert labor

Perform new cognition.

K-line templates

Reusable cognitive topology.

K-line artifacts

Compiled implementations of cognition.

Evaluation

Evidence that cognition works under a defined protocol.

Verification

Evidence that claimed execution actually happened.

Compute

Resources needed to run cognition.

Evidence and grounding

Trusted information needed to solve or verify problems.

This produces a richer market than simple model-token resale.

27. Attribution

Cognitive lineage may support long-term attribution.

Example:

Expert E
   ↓ contributed to
Episode P
   ↓ promoted into
K-line K
   ↓ compiled into
Artifact A
   ↓ used by
17,000,000 future executions

Future economic mechanisms may reward contributions based on downstream cognitive impact.

This is not required for the first protocol version but should remain possible through immutable lineage.

28. Hard Architectural Boundaries

The following boundaries are normative.

Kline

owns:
    cognition
    System 1/System 2
    K-lines
    episodes
    artifacts
    Coordinator
    Cognitive Compiler
    novelty
    learning
    local trust

Actum Compute

owns:
    expert discovery
    capability resolution
    markets
    pricing
    matching
    expert execution procurement
    artifact distribution
    evaluation procurement
    evidence procurement
    economic settlement orchestration

Actum

owns:
    verifiable acts
    commitments
    lineage
    evidence records
    finality
    settlement state
    asset transfers

TLSNotary / Distill-derived evidence layer

owns:
    authenticated remote execution evidence

ZeroK

owns:
    confidential verification

SurrealDB

owns:
    persistent local Mind state

Agent Plugins / MCP / A2A

owns:
    distribution
    discovery
    invocation
    agent communication

29. Core Invariants

The architecture SHALL preserve the following invariants.

1. A K-line describes cognition, not permission.

2. Capability resolution does not confer authority.

3. The local Mind controls activation.

4. Global sharing does not imply global trust.

5. Provenance is not equivalent to truth.

6. K-lines are provisional cognition and may decay or be superseded.

7. System 2 retains the ability to reject System-1 precedent.

8. K-line templates describe requirements rather than unnecessarily fixing vendors.

9. Concrete supplier/model identity remains available in episodes and evidence.

10. High-value model identity should be cryptographically evidenced where possible.

11. Actum Compute owns market economics; Kline does not.

12. Actum records verifiable state transitions; it does not decide local cognitive trust.

13. Installing an Agent Plugin does not itself authorize execution or economic participation.

14. Sensitive cognition and commercial data should be selectively disclosed whenever possible.

15. Successful System-2 cognition should be eligible for progressive compilation into System 1.

30. Positioning

The DCN should not be positioned principally as:

• a model;
• an LLM wrapper;
• an agent framework;
• a model marketplace;
• spare-token trading;
• a vector-memory system.

The platform is:

A distributed cognitive network that discovers how to solve problems, verifies the solution process, compiles successful reasoning into reusable K-lines, and distributes those learned cognitive structures across a society of specialized intelligences.

Kline provides the cognitive architecture.

Actum Compute provides the intelligence market.

Actum provides verifiable action, evidence, provenance, and settlement.

The Cognitive Compiler turns expensive discovery into increasingly abundant cognition.

31. Long-Term Thesis

A monolithic AGI attempts to place increasingly general capability inside a single model or system.

The DCN takes a different path.

It seeks generality by continuously increasing the region of problem space covered by:

• specialized expertise;
• reusable K-lines;
• higher-order K-line compositions;
• evaluated cognitive artifacts;
• compiled System-1 cognition.

General capability therefore emerges from a growing organization of specialized cognition rather than requiring every capability to exist within a single model.

The fundamental loop is:

UNKNOWN
   ↓
System 2
   ↓
solved
   ↓
validated
   ↓
K-line
   ↓
compiled
   ↓
System 1

The frontier continually moves outward.

The lasting asset of the network is therefore neither compute nor model access.

It is the accumulated body of verified, reusable, increasingly efficient cognitive capital.