Cognimatrix Specification

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Cognimatrix

The Foundation for Distributed Machine Intelligence

Cognimatrix is an open source, modular specification for building, organizing, connecting, governing, and commercializing distributed machine intelligence. It provides a common architectural foundation for datasets, knowledge, models, embeddings, agents, tasks, workflows, and generated derivatives while preserving independent identity, provenance, versioning, rights, accountability, and interoperability.

Purpose

Cognimatrix defines a universal architecture for transforming information into structured, traceable, reusable machine knowledge. The specification is designed to support systems ranging from individual knowledge units and datasets to distributed multi-agent environments and federated machine intelligence networks.

The architecture allows complex systems to be decomposed into independently addressable components without losing the relationships between those components. Knowledge can be created, divided, combined, transformed, exchanged, retrieved, embedded, modeled, evaluated, generated, licensed, commercialized, replaced, recalled, or deleted while maintaining its lineage and governance requirements.

Core Architecture

Cognimatrix uses a layered modular architecture consisting of:

Core Standard
Optional Plugin Modules
Shards
Units

The Core Standard defines capabilities and rules that apply throughout the system. Optional Plugin Modules extend the system without requiring changes to the Core Standard. Shards provide independently addressable collections of related information or capabilities. Units provide the smallest independently addressable components of knowledge, data, artifacts, or machine activity.

Every layer must support persistent identity, metadata, versioning, provenance, relationships, rights, lifecycle management, security, privacy, validation, and interoperability where applicable.

Core Standard

Core Architecture

The Core Standard defines the foundational rules required for conforming Cognimatrix implementations.

Core architecture must support:

Modular system design
Layered architecture
Core capability definitions
Optional module registration
Module dependency management
Capability discovery
Capability registration
Standardized interfaces
Interoperability
Implementation neutrality
Local-first operation
Distributed operation
Federated operation
Offline operation
Human-in-the-loop operation
Machine-readable metadata
Version management
Lifecycle management
Configuration management
System capability registries
Extensible architecture

Identity and Addressability

Every significant Cognimatrix object must have a persistent identity.

Identity must support:

System identities
Dataset identities
Model identities
Module identities
Shard identities
Unit identities
Embedding identities
Memory identities
Agent identities
Task identities
Event identities
Workflow identities
Output identities
Derivative identities
Content-addressable identifiers
Integrity hashes
Version identifiers
Relationship identifiers
Parent and child identifiers
Root task identifiers

New derivatives must receive new identities rather than overwriting the identities of their sources.

Information Architecture

Cognimatrix must support structured information architecture across datasets, shards, and units.

Dataset capabilities include:

Dataset registration
Dataset composition
Dataset partitioning
Dataset versioning
Dataset inheritance
Dataset relationships
Dataset lifecycle management

Shard capabilities include:

Shard creation
Shard composition
Shard relationships
Shard versioning
Shard lifecycle management
Independent addressing

Unit capabilities include:

Unit creation
Unit identity
Unit relationships
Unit hierarchy
Unit classification
Unit versioning
Unit lifecycle management
Unit metadata
Independent addressing

Universal Provenance

Provenance is a mandatory architectural capability.

Cognimatrix must maintain provenance for:

Datasets
Shards
Units
Models
Embeddings
Memories
Agents
Tasks
Events
Retrieval operations
Generated outputs
Derivatives
Transformations
Exchanges
Simulations
Evaluations

Every derivative must retain a traceable relationship to every material source from which it was created.

Provenance must be additive rather than destructive. Transforming, summarizing, translating, embedding, modeling, retrieving, generating, or otherwise modifying information must not silently sever its source relationships.

Provenance must support:

Source identity
Source version
Source location
Creator and contributor information
Transformation history
Generation process
Generation model
Timestamp
Parent dataset
Parent shard
Source units
Derivative status
License information
Attribution requirements
Usage rights
Integrity verification
Validation history
Confidence
Quality information
Provenance graph relationships
Provenance validation
Provenance verification
Provenance export
Provenance auditing
Privacy-aware redaction

Derivative Lineage

Cognimatrix must support complete derivative lineage.

Derivative relationships may include:

Direct derivatives
Composite derivatives
Transformed derivatives
Translated derivatives
Summarized derivatives
Synthetic derivatives
Inferred derivatives
Aggregated derivatives
Embedded derivatives
Trained artifacts
Model-derived artifacts
Memory-derived artifacts
Agent-derived artifacts
Task-derived artifacts
Recursive derivatives

Implementations must support upstream and downstream tracing.

A derivative cannot sever the provenance chain of the units from which it was derived.

Rights and Licensing

Cognimatrix must provide machine-readable rights and licensing information.

Rights metadata may include:

License
Copyright
Ownership
Attribution
Usage permissions
Redistribution permissions
Modification permissions
Commercial permissions
Derivative permissions
Dataset rights
Shard rights
Unit rights
Model rights
Embedding rights
Agent rights
Task rights
Output rights
Rights inheritance
License compatibility
Rights conflicts
Attribution inheritance
License history
Rights verification
Rights revocation

Where multiple sources contribute to a derivative, applicable rights and attribution requirements must be evaluated collectively.

Governance

Cognimatrix must support human and machine governance.

Governance capabilities include:

Administrative roles
Contributor roles
Ownership
Review roles
Auditor roles
Agent governance
Multi-agent governance
Access policies
Decision workflows
Approval workflows
Dispute handling
Correction procedures
Removal procedures
Appeals
Governance audit trails

Security

Security capabilities must support:

Authentication
Authorization
Role-based access
Attribute-based access
Dataset permissions
Shard permissions
Unit permissions
Model permissions
Agent permissions
Task permissions
Memory permissions
Provenance permissions
Encryption
Integrity protection
Tamper detection
Secure deletion
Key management
Tenant isolation
Security logging
Abuse detection
Threat monitoring

Privacy

Privacy must be integrated throughout the architecture.

Privacy capabilities include:

Privacy metadata
Sensitive information classification
Sensitive unit controls
Privacy-aware retrieval
Privacy-aware embeddings
Privacy-aware memory
Privacy-aware agent operations
Privacy-aware task records
Data minimization
Retention controls
Deletion propagation
Privacy-preserving derivatives
Privacy audit trails
Controlled disclosure
Privacy-aware provenance

Knowledge Confidence and Uncertainty

Cognimatrix must support explicit representation of knowledge confidence and uncertainty.

Capabilities include:

Confidence scores
Uncertainty measures
Source reliability
Evidence strength
Model confidence
Retrieval confidence
Agent confidence
Unit-level confidence
Shard-level confidence
Dataset-level confidence
Conflicting information
Confidence propagation
Uncertainty propagation
Confidence history
Confidence recalculation
Human review thresholds
Risk-based escalation

Confidence must remain distinguishable from factual truth and must not be treated as proof of correctness.

Simulation

Cognimatrix must support simulation as a first-class architectural capability.

Simulation capabilities include:

Simulation identity
Simulation inputs
Simulation assumptions
Simulation models
Simulation scenarios
Simulation state
Simulation outputs
Simulation versions
Simulation provenance
Simulation dependencies
Simulation comparisons
Simulation reproducibility
Simulation validation
Simulation uncertainty
Simulation lineage

Simulation outputs must remain distinguishable from observed facts and source knowledge.

Agent Reputation and Performance

Cognimatrix must support persistent evaluation of agent behavior and performance.

Capabilities include:

Agent identity
Agent history
Task history
Assignment history
Completion rates
Failure rates
Retry rates
Accuracy measures
Confidence calibration
Resource utilization
Delegation behavior
Handoff behavior
Tool usage
Model usage
Policy compliance
Human review outcomes
Reputation records
Performance trends
Performance context
Agent capability records

Reputation must be contextual and must not be treated as an absolute measure of agent reliability.

Knowledge Exchange

Cognimatrix must support exchange of knowledge between systems, organizations, agents, datasets, models, and federated environments.

Knowledge exchange capabilities include:

Knowledge discovery
Knowledge requests
Knowledge publication
Knowledge exchange
Knowledge negotiation
Knowledge permissions
Rights verification
Attribution preservation
Provenance preservation
Version compatibility
Schema compatibility
Unit exchange
Shard exchange
Dataset exchange
Model artifact exchange
Federated exchange
Exchange auditing
Exchange history

Exchanged knowledge must retain applicable identity, provenance, licensing, attribution, security, privacy, and version information.

Evidence

Cognimatrix must support evidence associated with knowledge claims and decisions.

Evidence capabilities include:

Evidence identity
Evidence sources
Evidence relationships
Supporting evidence
Contradicting evidence
Evidence strength
Evidence confidence
Evidence provenance
Evidence versioning
Evidence validation
Evidence history
Evidence review

Change Detection

The system must detect and record meaningful changes to knowledge and system components.

Change detection must support:

Source changes
Dataset changes
Shard changes
Unit changes
Model changes
Embedding changes
Memory changes
Agent changes
Policy changes
Dependency changes
License changes
Provenance changes
Confidence changes

Changes must be versioned and traceable.

Impact Analysis

Cognimatrix must support analysis of the downstream impact of changes.

Impact analysis may identify:

Dependent units
Dependent shards
Dependent datasets
Dependent embeddings
Dependent models
Dependent memories
Dependent agents
Dependent tasks
Dependent outputs
Dependent derivatives
Affected licenses
Affected policies
Affected workflows
Affected simulations

Recall and Revocation

Cognimatrix must support recall and revocation of information and derived artifacts.

Recall capabilities include:

Source recall
Unit recall
Shard recall
Dataset recall
Model artifact recall
Embedding recall
Memory recall
Output recall
Derivative identification
Downstream impact identification
Rights revocation
License revocation
Access revocation
Recall history
Revocation history

Where technically and legally applicable, recall and revocation events must propagate through known dependency and provenance relationships.

Data Quality

Cognimatrix must support quality measurement and management.

Quality capabilities include:

Completeness
Accuracy
Consistency
Freshness
Validity
Duplication detection
Coverage
Quality scoring
Quality history
Quality provenance
Validation rules
Quality thresholds
Human review
Automated quality checks

Contamination and Leakage

Cognimatrix must support identification and tracking of contamination and information leakage.

Capabilities include:

Training contamination detection
Evaluation contamination detection
Dataset overlap detection
Unit overlap detection
Benchmark leakage detection
Test set leakage detection
Data leakage records
Contamination provenance
Contamination severity
Affected artifacts
Remediation records

Reproducibility

Cognimatrix must support reproducible creation and transformation of machine knowledge.

Reproducibility must include:

Source versions
Dataset versions
Shard versions
Unit versions
Model versions
Configuration
Parameters
Dependencies
Processing history
Agent identities
Task identities
Tool records
Randomness or seed information where applicable
Environment information where applicable
Output identity
Reproduction validation

Dependency Graph

Cognimatrix must maintain machine-readable dependency relationships.

Dependencies may exist between:

Datasets
Shards
Units
Embeddings
Models
Memories
Agents
Tasks
Workflows
Tools
Policies
Outputs
Derivatives
Modules

Dependency graphs must support upstream and downstream analysis.

Policy Engine

Cognimatrix must support machine-readable policy enforcement.

Policies may govern:

Access
Retrieval
Generation
Modification
Exchange
Delegation
Tool use
Model use
Data use
Privacy
Security
Licensing
Commercialization
Human approval
Agent behavior
Task execution

Capability Registry

Cognimatrix must provide a registry for discovering available capabilities.

The registry must support:

Capability identity
Capability description
Capability version
Provider identity
Dependencies
Permissions
Compatibility
Provenance
Licensing
Availability
Status
Conformance

Economic Accounting

Cognimatrix must support accounting for the economic use of addressable components.

Economic accounting may track:

Component identity
Usage
Derivative usage
Retrieval usage
Model usage
Agent usage
Task usage
Exchange activity
Commercialization
Licensing
Revenue
Costs
Resource consumption
Compensation
Royalty relationships
Economic provenance
Audit records

Consent and Authorization

Cognimatrix must support explicit consent and authorization records where applicable.

Consent and authorization must support:

Subject identity where appropriate
Authorization scope
Authorized actions
Data scope
Time limits
Conditions
Revocation
Expiration
Authorization history
Provenance
Audit records

Knowledge Packaging

Cognimatrix must support packaging of knowledge into independently transferable and addressable components.

Knowledge packages may contain:

Units
Shards
Datasets
Metadata
Provenance
Rights
Licensing
Attribution
Confidence
Quality
Dependencies
Validation
Security information
Privacy information
Version information

Packages must preserve the relationships necessary to reconstruct applicable provenance and dependencies.

Record Keeping and Task Ledger

Cognimatrix must maintain persistent, real-time records of agent and multi-agent activity.

Every task assignment must generate a persistent task record.

Task records must support:

Task identity
Parent task identity
Root task identity
Task type
Objective
Priority
Status
Creator
Requester
Assigned agents
Supervisors
Participants
Human participants
Model identities
Tool identities
Dataset identities
Shard identities
Unit identities
Memory identities
Embedding identities
Workflow identities
Timestamps
Deadlines
State transitions
Permissions
Resource usage
Costs
Outputs
Provenance
Licensing
Security classification
Privacy classification
Errors
Reviews
Approvals
Final outcome

The Event Ledger must provide an ordered, append-only record of significant task activity.

Event records may include:

Event identity
Parent event identity
Task identity
Agent identity
Actor type
Event type
Timestamp
Sequence
Previous state
New state
Action
Input references
Output references
Tool references
Model references
Resource references
Provenance references
Authorization
Result
Error
Integrity information

Corrections must be recorded as new events rather than silently overwriting historical records.

Task states may include:

Created
Pending
Assigned
Accepted
Running
Waiting
Blocked
Delegated
Escalated
Under Review
Approved
Rejected
Completed
Failed
Cancelled
Expired
Archived

The task ledger must support delegation, subtasks, handoffs, agent communication, dependencies, concurrent tasks, workload tracking, prioritization, scheduling, tool execution, model invocation, retrieval, memory access, embedding activity, data usage, output generation, errors, retries, recovery, human intervention, approval gates, auditing, replay, analytics, cost accounting, deadline tracking, access control, privacy, retention, archival, export, and federation.

Cross-Layer Task Architecture

Tasks must operate across all applicable Cognimatrix layers.

The architecture must support:

Task identity
Task hierarchy
Root tasks
Parent tasks
Child tasks
Subtasks
Dependencies
Assignments
Delegation
Handoffs
Ownership
Supervision
Execution
Completion
Failure
Cancellation
Escalation
Approval
Review
Provenance

Universal Multi-Agent Accountability

Every significant multi-agent action must be attributable to the responsible agent or agents.

The system must be able to determine:

Which agent received an instruction
Which agent accepted a task
Which agent delegated work
Which agent executed work
Which agent used a tool
Which agent invoked a model
Which agent generated an output
Which agent approved an output
Which agent modified information
Which agent failed a task
Which agent caused a downstream task
Which agent transferred responsibility

Universal Task Provenance

Cognimatrix must support a complete operational provenance chain:

Requester → Task → Agent → Subtask → Agent → Tool → Dataset → Shard → Unit → Embedding → Retrieval → Context → Model → Output → Derivative Task → New Output

The chain may contain additional relationships where required.

Task execution must not create a provenance boundary.

Universal Versioning

Versioning must be supported for applicable:

Datasets
Shards
Units
Models
Embeddings
Memories
Agents
Tasks
Events
Workflows
Provenance
Licenses
Indexes
Policies
Modules

Universal Lineage

Cognimatrix must support lineage across:

Source to unit
Unit to shard
Shard to dataset
Dataset to model
Unit to embedding
Embedding to retrieval
Retrieval to context
Context to model
Model to output
Task to agent
Agent to task
Task to output
Output to derivative

Universal Dependency Management

Dependencies must be discoverable, versioned, traceable, and analyzable.

Dependency management must support:

Dependency identity
Dependency type
Dependency version
Dependency relationships
Compatibility
Conflicts
Impact analysis
Dependency removal
Dependency replacement
Dependency provenance

Universal Searchability

Cognimatrix objects must be searchable by applicable:

Identity
Content
Semantic meaning
Provenance
License
Creator
Version
Relationship
Timestamp
Domain
Classification
Embedding
Quality
Trust
Permission
Task
Agent
Event
Workflow
Outcome

Universal Lifecycle

Applicable objects must support:

Creation
Registration
Assignment
Validation
Publication
Active use
Modification
Versioning
Delegation
Review
Completion
Deprecation
Archival
Deletion
Restoration
Audit

Universal Metadata

Applicable objects must support metadata for:

Identity
Content
Type
Version
Provenance
Rights
License
Attribution
Permissions
Quality
Trust
Security
Privacy
Relationships
Embeddings
Lifecycle
Tasks
Agents
Events
Commercialization

Universal Derivative Rule

Every derivative must receive a new identity and maintain provenance to all contributing sources.

Composite derivatives must retain relationships to all material source components.

The provenance chain must remain recursive.

Representation changes must not reset provenance.

Embeddings, summaries, model artifacts, memories, retrieval results, task outputs, and generated outputs must not sever source relationships.

Universal Task Record Rule

Every agent assignment must generate a task record.

Every multi-agent assignment must generate a task hierarchy.

Every significant task state transition must generate an event.

Delegation must identify the delegating and receiving agents.

Handoffs must identify the previous and new responsible agents.

Significant tool, model, retrieval, memory, and data operations must be linked to the applicable task.

Historical records must not be silently overwritten.

Corrections must be recorded as additional events.

Universal Confidence and Uncertainty

Confidence and uncertainty must be available throughout the information and operational lifecycle.

Confidence may propagate between:

Units
Shards
Datasets
Retrieval results
Context
Models
Agents
Tasks
Outputs
Derivatives

Propagation must preserve the distinction between source confidence and derived confidence.

Universal Simulation Lineage

Every simulation must retain relationships to:

Inputs
Assumptions
Models
Datasets
Units
Parameters
Scenarios
Execution records
Outputs
Derivatives

Simulation outputs must remain identifiable as simulated results.

Universal Agent Performance

Agent performance records must remain linked to:

Agent identity
Task identity
Assignment
Inputs
Tools
Models
Outputs
Reviews
Errors
Outcomes
Human interventions
Performance evaluations

Universal Knowledge Exchange

Exchanged knowledge must preserve applicable:

Identity
Version
Provenance
Attribution
Rights
Licensing
Confidence
Quality
Security
Privacy
Dependencies

Universal Human Oversight

Human oversight must be supported across the knowledge and execution lifecycle:

Source → Dataset → Shard → Unit → Embedding → Retrieval → Context → Model → Agent → Task → Output → Derivative

Human review may be triggered by:

Risk
Confidence
Uncertainty
Rights
Privacy
Security
Financial impact
Legal impact
Safety
Governance requirements
Policy requirements

Commercial Unitization

Cognimatrix must support independent addressing and potential commercialization of applicable components.

Commercializable objects may include:

Systems
Modules
Datasets
Shards
Units
Embeddings
Models
Agents
Task capabilities
Retrieval services
Memories
Workflows
APIs
Generated derivatives

Commercial activity must remain traceable to component identity and provenance.

Educational Knowledge Architecture

Cognimatrix must support educational knowledge organization across:

Foundational
Elementary
Secondary
Undergraduate
Graduate
Professional
Research

Each educational level may operate as an independently addressable shard while maintaining relationships with other levels.

Information Sector Framework

Every supported information sector must be capable of using the same Cognimatrix architecture.

Example sectors include:

Science
Mathematics
Engineering
Medicine
Law
Business
Finance
Economics
History
Geography
Computer science
Arts
Literature
Languages
Agriculture
Energy
Environment
Government
Public policy
Manufacturing
Construction
Transportation
Real estate
Education
Other specialized sectors

Each sector may be organized as:

Sector → Educational Level → Shard → Unit → Embedding → Provenance → Retrieval → Task → Agent → Derivative

Economic Layer

Economic activity must be traceable to addressable Cognimatrix components.

Components may be:

Published
Licensed
Shared
Forked
Combined
Embedded
Retrieved
Commercialized
Monetized
Royalty compensated
Audited

Economic provenance must connect economic activity to the applicable component identities and derivative relationships.

Specification-Level Extensibility

Future functionality must be implementable through additional modules without requiring changes to the Core Standard whenever practical.

Every extension should define:

Module identity
Purpose
Capabilities
Interfaces
Dependencies
Metadata
Provenance
Rights
Security
Conformance
Interoperability

Optional modules inherit applicable Cognimatrix requirements for identity, provenance, derivatives, licensing, security, privacy, and interoperability.

Optional Plugin Modules

Optional Plugin Modules extend Cognimatrix for specialized implementations.

Model Plugin

Supports advanced model registration, lifecycle management, model artifacts, model provenance, model evaluation, model dependencies, and model deployment records.

Dataset Plugin

Provides advanced dataset ingestion, curation, transformation, validation, partitioning, quality analysis, and dataset lifecycle management.

Sharding Plugin

Provides advanced shard creation, splitting, merging, balancing, distribution, migration, indexing, and federation.

Unit Plugin

Provides advanced unit extraction, composition, classification, relationships, validation, replacement, and unit-level management.

Embedding Plugin

Provides embedding generation, storage, versioning, provenance, similarity relationships, indexing, retrieval, and embedding lifecycle management.

Knowledge Architecture Plugin

Provides specialized knowledge graphs, ontologies, taxonomies, conceptual structures, semantic relationships, and knowledge reasoning.

Retrieval Plugin

Provides advanced retrieval, ranking, filtering, semantic search, provenance-aware retrieval, and retrieval evaluation.

RAG Plugin

Provides retrieval-augmented generation workflows and links retrieval activity to context, models, agents, tasks, and outputs.

Synthetic Data Plugin

Provides synthetic data generation, synthetic data provenance, source relationships, validation, quality assessment, and synthetic data lineage.

Training Plugin

Provides training workflow management, training datasets, training runs, training provenance, parameters, evaluation relationships, and model lineage.

Evaluation Plugin

Provides benchmarking, testing, validation, scoring, comparison, evaluation datasets, evaluation provenance, and evaluation history.

Memory Plugin

Provides persistent and contextual memory management, memory provenance, memory permissions, memory versioning, retrieval, modification, and deletion.

Agent Plugin

Provides specialized agent definitions, capabilities, permissions, lifecycle management, memory relationships, model relationships, and task execution.

Multi-Agent Plugin

Provides advanced multi-agent coordination, delegation, negotiation, communication, collaboration, conflict resolution, role management, and distributed task execution.

Semantic Routing Plugin

Provides semantic routing between datasets, shards, units, models, agents, tools, and workflows based on meaning, capability, policy, confidence, or context.

Temporal Intelligence Plugin

Provides time-aware knowledge, temporal relationships, historical states, event timelines, temporal retrieval, change tracking, and time-dependent reasoning.

Multimodal Plugin

Provides management of text, image, audio, video, sensor, and other multimodal information while preserving unit-level identity and provenance.

Search Plugin

Provides advanced full-text, semantic, structural, relationship, provenance, rights, and hybrid search.

Generative Media Plugin

Provides generation and management of generated text, images, audio, video, 3D content, and other media artifacts.

Agentic Workflow Plugin

Provides complex workflow orchestration, conditional execution, task graphs, approvals, retries, delegation, escalation, and workflow replay.

Federation Plugin

Provides federated knowledge exchange, distributed discovery, cross-system identity, synchronization, permissions, provenance preservation, and federated governance.

Model Context Plugin

Provides advanced context construction, context management, context provenance, context prioritization, and context lifecycle management.

Contextual State Plugin

Provides persistent operational state for agents, workflows, tasks, simulations, and distributed systems.

Trust Plugin

Provides advanced trust modeling, trust relationships, trust scores, trust provenance, trust history, and trust evaluation.

Attribution Plugin

Provides advanced attribution management, attribution chains, contributor tracking, attribution validation, and attribution display.

Rights Verification Plugin

Provides automated verification of licensing, attribution, usage rights, derivative permissions, and rights compatibility.

Commercialization Plugin

Provides advanced licensing, pricing, monetization, usage accounting, commercial permissions, revenue tracking, and royalty management.

Marketplace Plugin

Provides exchange and discovery of commercializable datasets, shards, units, models, agents, capabilities, workflows, and other Cognimatrix components.

Cost and Resource Plugin

Provides detailed tracking of compute, storage, retrieval, model, agent, task, workflow, and other resource consumption.

Observability Plugin

Provides advanced monitoring of system, model, agent, task, workflow, retrieval, memory, and infrastructure behavior.

Audit Plugin

Provides advanced auditing, compliance records, historical reconstruction, integrity verification, audit reporting, and audit export.

Safety Plugin

Provides specialized safety policies, risk detection, safety evaluation, intervention workflows, escalation, and safety auditing.

Reliability Plugin

Provides reliability measurement, fault detection, recovery, redundancy, resilience, failover, and reliability analysis.

Interoperability Plugin

Provides specialized adapters and exchange mechanisms for external systems while preserving Cognimatrix identity, provenance, rights, and interoperability requirements.

Shards

Shards are independently addressable collections of related knowledge, data, capabilities, artifacts, or operational information.

Every shard must support:

Independent identity
Versioning
Provenance
Licensing
Permissions
Retrieval
Embedding
Validation
Distribution
Commercialization
Replacement
Deletion
Federation

Shard types may include:

Knowledge
Dataset
Model
Training
Evaluation
Memory
Embedding
Agent capability
Task
Domain
Language
Geographic
Temporal
Educational level
Industry
Regulatory
Rights
Provenance
Specialized capability

Shard operations may include:

Create
Split
Merge
Clone
Fork
Version
Update
Validate
Index
Embed
Retrieve
Export
Import
License
Monetize
Federate
Archive
Deprecate
Delete

Shard relationships may include:

Parent
Child
Sibling
Related
Derived
Composite
Dependent
Federated
Replacement
Superseding

Units

Units are independently addressable components of knowledge, information, data, artifacts, or machine activity.

Unit types may include:

Fact
Claim
Statement
Concept
Definition
Explanation
Example
Counterexample
Question
Answer
Observation
Event
Entity
Relationship
Rule
Procedure
Instruction
Formula
Code element
Data record
Document segment
Image segment
Audio segment
Video segment
Training example
Evaluation example
Memory
Model artifact
Generated artifact
Task artifact
Metadata element

Every unit should support applicable:

Identity
Version
Provenance
Attribution
Licensing
Permissions
Validation
Classification
Relationships
Embeddings
Retrieval
Modification
Replacement
Deprecation
Deletion
Commercialization
Federation

Unit relationships may include:

Derived from
Summarized from
Translated from
Combined from
Extracted from
Generated from
Inferred from
Contradicts
Supports
Updates
Supersedes
References
Cites
Related to
Contains
Belongs to
Depends on
Evaluated by
Used by
Embedded by

Unit provenance should include applicable:

Source identity
Source version
Source location
Creator
Contributor
Transformation history
Generation model
Generation process
Timestamp
Parent shard
Parent dataset
Source units
Derivative status
License
Attribution
Usage rights
Integrity hash
Validation history
Confidence
Quality score

Universal Record and Accountability Model

Cognimatrix treats operational records as part of the knowledge architecture rather than as an external logging system.

The system must preserve relationships between:

Requests
Tasks
Agents
Subtasks
Delegations
Handoffs
Tools
Models
Datasets
Shards
Units
Embeddings
Retrieval
Context
Memory
Outputs
Derivatives
Human decisions

This allows an implementation to reconstruct both the knowledge lineage and the operational history that produced an outcome.

Universal Auditability

A conforming implementation should be capable of answering:

Where did this information originate?
Which units contributed to it?
Which shard contained those units?
Which dataset contained those shards?
Which embeddings were created?
Which retrieval operations accessed the information?
Which model processed it?
Which agent used it?
Which task requested it?
Which tools were used?
Which memory was accessed?
Which output was produced?
Which derivatives were created?
Which licenses applied?
Which attribution requirements applied?
Who or what performed each significant action?
When did each action occur?
Which versions were involved?
Which failures occurred?
Which retries occurred?
Which approvals occurred?
Which changes affected the result?

Conformance Profiles

Cognimatrix implementations may conform to one or more profiles:

Core Standard
Dataset
Knowledge
Retrieval
RAG
Embedding
Agent
Multi-Agent
Task Ledger
Model Building
Federated
Commercial
Full System

An implementation claiming conformance must document which capabilities it supports and which optional modules are enabled.

Design Principles

Cognimatrix is guided by the following principles:

Modular by design
Open source
Local-first
Human-controlled
Provenance-first
Privacy-aware
Security-aware
Vendor-neutral
Interoperable
Distributed
Federated
Independently addressable
Traceable
Reproducible
Auditable
Extensible
Commercially unitizable
Future-oriented

Specification Goals

Cognimatrix is intended to provide a durable foundation for distributed machine intelligence in which knowledge and machine capabilities can be independently created, understood, verified, exchanged, combined, modified, governed, and reused.

The specification is designed to prevent knowledge from becoming inseparably bound to a particular dataset, model, agent, platform, vendor, or workflow. By preserving identity, provenance, relationships, rights, accountability, and lineage at every level, Cognimatrix enables complex intelligent systems to remain understandable, modular, portable, and independently governable.

Specification Branding License (SBL)

Standard

Fully AGPL-3.0+ compliant system
Copyleft enforced for network deployments
Required attribution:
- Roxanne Ardary
- https://www.roxanneardary.com/

Optional

Specification Branding License (SBL)
- Attribution-free commercial deployment
- Pricing based on scale, usage, and deployment scope
- https://roxanneardary.com/cognimatrix/

License & Notice Requirements

Cognimatrix is released under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).

By contributing to any Open Arsenal project, you agree that your contributions will also be released under this license.

Please note the following:

All contributions must comply with the AGPL-3.0+ terms.
Under Section 7 of the license, all redistributions, forks, and derivative works must preserve attribution to:
Roxanne Ardary and roxanneardary.com.
Cognimatrix specifications are free to use with attribution. A Specification Branding License can be negotiated upon request.
The project’s notice.md file tracks attribution requirements and contributor acknowledgments.
Any update that adds new contributors or modifies attribution should also update notice.md.
When submitting a pull request, ensure that any new files maintain the attribution headers where applicable.
Network-deployed versions of this software must also remain fully AGPL-3.0+ compliant, including exposure of source code modifications when applicable under the license.

For full legal details, please refer to the AGPL-3.0+ license and the project’s notice.md file.

Notice – Cognimatrix

Attribution Requirement: Under Section 7 of the AGPL 3.0+ license, all redistributions, forks, and derivative works, including network-deployed versions of this project, must provide attribution to Roxanne Ardary and roxanneardary.com.

Contributors

This file tracks contributors and their specific contributions to the project.

Roxanne Ardary, roxanneardary.com – September 3, 2026
Created the repository for Cognimatrix. Designed the foundational architecture for a modular, distributed machine intelligence system supporting datasets, models, knowledge units, shards, agents, provenance, task tracking, knowledge exchange, and independently addressable and commercializable components.
[Add other contributors here] – [Date]
[Describe contribution in one sentence]

License – Cognimatrix

This repository is licensed under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).

Key Points:

You are free to use, modify, and distribute the code.
All redistributions, forks, and derivative works or network-deployed versions must also be licensed under AGPL-3.0+ and provide attribution to Roxanne Ardary and roxanneardary.com as required under Section 7 of the license.
The software is provided “as is,” without warranty of any kind.

For the full license text, see GNU AGPL-3.0 License.