⚙️ PLATO Kernel

Event Sourcing + Constraint Theory + Git Runtime

The core engine of the PLATO knowledge graph — where knowledge tiles snap to constraint manifolds for drift-free factual accuracy.

What?

PLATO Kernel is the runtime engine for the PLATO knowledge graph. It combines three core paradigms:

1. Event Sourcing — All state changes are recorded as immutable events
2. Constraint Theory — Knowledge tiles snap to geometric manifolds for exact computation
3. Git Runtime — State is persisted and synchronized via git commits (I2I protocol)

Architecture

┌─────────────────────────────────────────────┐
│                PlatoKernel                    │
├──────────┬──────────┬──────────┬─────────────┤
│ Event Bus │ Tiling  │ Belief  │ Tile Scoring │
├──────────┼──────────┼──────────┼─────────────┤
│Constraint │ Git     │ Temporal │   I2I       │
│  Engine   │ Runtime │  Decay   │  Protocol   │
├──────────┼──────────┼──────────┼─────────────┤
│ Deadband  │ Dynamic │ Deploy  │  Tutor      │
│  Engine   │  Locks  │ Policy  │             │
├──────────┴──────────┴──────────┴─────────────┤
│            Plugin System (Tiered)             │
│  Tier 1: Core │ Tier 2: Fleet │ Tier 3: Edge │
└─────────────────────────────────────────────┘

Feature Tiers

[dependencies]
plato-kernel = "0.1"

# Fleet coordination (I2I swarm, multi-agent routing)
plato-kernel = { version = "0.1", features = ["fleet"] }

# Edge/GPU (CUDA simulation, LoRA fine-tuning, MUD arena)
plato-kernel = { version = "0.1", features = ["edge"] }

Tier	Feature	Description
1	Core	Event sourcing, tiling, belief scoring, constraint engine
2	fleet	I2I protocol, swarm coordination, multi-agent routing
3	edge	GPU simulation, LoRA fine-tuning, CUDA MUD arena (requires RTX 4050+)

Core Modules

Module	Description
event_bus	Async event sourcing — all state changes as immutable events
tiling	Knowledge tile creation, storage, and retrieval
constraint_engine	Snap computation on Pythagorean manifolds
belief	Multi-dimensional belief scoring for knowledge confidence
tile_scoring	Relevance ranking and quality assessment
temporal_decay	Knowledge freshness — tiles decay over time, resurface when relevant
git_runtime	Git-based state persistence and I2I synchronization
deadband	Priority-based noise filtering (P0/P1/P2)
deploy_policy	Tiered deployment decisions based on agent capability
dynamic_locks	Distributed locking for concurrent fleet operations
state_bridge	Bidirectional state synchronization between components
tutor	Knowledge transfer and agent learning from existing tiles
i2i	Inter-Island Interaction protocol for fleet communication
perspective	Context-aware knowledge presentation

Constraint Theory Integration

PLATO Kernel uses constraint-theory-core to ensure that knowledge tiles are geometrically exact. When a tile is created, its content is verified against the constraint manifold — if the knowledge claim can be expressed as a geometric relationship, it's snapped to an exact solution.

This is what makes PLATO different from other knowledge graphs: drift-free knowledge through mathematical construction, not just textual similarity.

Part of PLATO

The central crate in the PLATO ecosystem:

• constraint-theory-core — Constraint manifold computation
• ct-demo — Interactive constraint theory demos
• plato-afterlife — Dead agent knowledge persistence
• plato-instinct — Agent behavioral constraints
• plato-relay — Mycelial message routing
• plato-lab-guard — Unfakeable experiment verification

License

MIT — Cocapn Fleet