Core Architectural Patterns

This document outlines the core architectural patterns that form the foundation of the NERV/Matrix architecture.

Reactive Event Mesh

The Reactive Event Mesh forms the “nervous system” of Atlas, allowing components to communicate without direct coupling.

Concept

Rather than connecting components through direct method calls, the event mesh creates a decentralized communication layer where:

1. Components emit events without knowledge of subscribers
2. Components subscribe to events without knowledge of publishers
3. Events can be observed, filtered, transformed, and composed

Benefits for Atlas

• Decoupling: Components can evolve independently
• Observability: Events can be logged, analyzed, and replayed
• Extensibility: New components can integrate by subscribing to existing events
• Testing: Components can be tested in isolation with simulated events

Temporal Versioning

Temporal versioning treats every state change as immutable, creating a timeline of system states rather than mutations.

Concept

Instead of modifying objects in place, each change creates a new version with:

1. A unique identifier
2. The delta from the previous version
3. Metadata about the cause of the change
4. Temporal relationship to other versions

Benefits for Atlas

• Time Travel: Inspect and revert to any previous system state
• Auditing: Every change is documented with its cause
• Analysis: Track how state evolves over time
• Concurrency: Simplifies concurrent operations by avoiding shared mutable state

Perspective Shifting

Perspective shifting allows components to adapt their abstraction level dynamically based on the observer’s needs.

Concept

Components provide multiple interfaces or views based on:

1. The observer’s role or permissions
2. The required level of detail
3. The current context or operation
4. The stage in the processing pipeline

Benefits for Atlas

• Simplified Interfaces: Present only relevant information for each use case
• Progressive Disclosure: Reveal complexity gradually as needed
• Adaptable Views: Transform data based on context
• Separation of Concerns: Keep raw data separate from presentation logic

State Projection

State projection stores minimal deltas and projection functions rather than complete states.

Concept

Instead of storing the full state at each change:

1. Store the initial state and subsequent changes (deltas)
2. Apply projection functions to construct any desired view of the state
3. Allow different projections for different use cases

Benefits for Atlas

• Efficiency: Store only the changes, not redundant state copies
• Flexibility: Generate different views from the same history
• Analysis: Understand how specific changes impact the final state
• Space Efficiency: Minimal storage requirements for historical states

Effect Systems

Effect systems make side effects explicit and trackable, originally from functional programming.

Concept

Instead of allowing implicit side effects, an effect system:

1. Explicitly declares what effects an operation might have
2. Tracks effects through the call stack
3. Allows controlled composition of effectful operations
4. Enables reasoning about side effects statically

Benefits for Atlas

• Transparency: Clear documentation of what operations do
• Predictability: Easier reasoning about program behavior
• Testability: Side effects can be mocked or verified
• Composition: Complex operations from simple ones with controlled effects

Quantum Partitioning

Quantum partitioning breaks operations into parallelizable units that maintain relationships.

Concept

Unlike traditional parallel programming models, quantum partitioning:

1. Divides work into “quantum units” with explicit dependencies
2. Schedules execution dynamically based on runtime conditions
3. Maintains cross-cutting relationships between units
4. Allows both sequential and parallel composition

The name comes from the concept of quantum superposition - units exist in a state of potential execution until their dependencies are resolved.

Benefits for Atlas

• Concurrency: Automatic parallelism without explicit thread management
• Declarative: Express relationships without imperative scheduling
• Adaptability: Scales from single-threaded to many-core execution
• Safety: Automatically detects dependency cycles