Build Spec Active Cognitive AI Architecture

SABLE

Infrastructure Simulator — Cognitive Engine for Operations

Synthetic training data generator for a novel AI architecture. Creates realistic infrastructure topologies, injects failures, simulates cascades through dependency graphs, and captures every step as structured training data. The model trained on this reasons structurally, causally, spatially. Language is the interface. The intelligence underneath is structural.

GNN POMDP Causal Reasoning State Space Models Infrastructure Ops PyTorch
Components 21 Infrastructure primitives
Failure Modes 87 Across all components
Dependency Types 9 Edge classifications
Failure Categories 8 Scenario generators
Purpose
Mission

SABLE is a synthetic training data generator for a novel AI architecture. It creates realistic infrastructure topologies, injects failures, simulates how those failures cascade through dependency graphs, and captures every step as structured training data.

The model trained on this data will reason about systems the way an operator reasons — structurally, causally, spatially. Language is the interface. The intelligence underneath is structural.

"The problem I am seeing is that all AI is mostly language models. That's what AI is about. Language, text. Why is that the focus and how do we push through that to something else?"
Current Phase
Timeline
Registry
Component Types
Graph Edges
Dependency Types
HARD
Target fails → source fails
SOFT
Target fails → source degrades
REDUNDANT
Unaffected unless ALL redundant targets fail
Scenario Generation
Failure Categories
God View — Ground Truth
  • Full topology, all states
  • Complete cascade trace
  • Every dependency status
  • Used as training LABELS
Operator View — Partial Observability
  • Only monitored components visible
  • State changes delayed by latency
  • Behind failures = "unknown"
  • Includes false positives & noise
The model learns to reason from the Operator View and reconstruct the God View. This is the core skill.
Single model, heterogeneous internal structure
Model Architecture
System Architecture
Telemetry
Logs
Topology
Events
State Ingestion Pipeline
Shared Latent State Space
Pillar I
Causal Graph Reasoning
Models how components relate and failures cascade
GNN // GRAPH ATTENTION
Pillar II
Decision Under Uncertainty
Evaluates decision trees with incomplete information
POMDP // MCTS // BAYESIAN
Pillar III
Multimodal State Repr.
Ingests structured data — telemetry, logs, topology
SSM // MAMBA // S4
Natural Language Interface — The Last Mile
GPU
RTX 5090
32GB VRAM
System RAM
64GB DDR5-6000
CL30-40-40-96
Target Model
100-500M params
Proof of concept
Training Data
100K scenarios
Synthetic generation