Inspect learning and computation
Understand what each cognitive and neural view controls.
These views expose engine capabilities beyond storing and retrieving a record. Choose one because it fits a task, not because every application needs to enable or tune it.
| Workspace area | What it represents | Learn more |
|---|---|---|
| Cognitive Cycle | A cycle that coordinates context and processing | Reasoning and cycles |
| Neurosymbolic | Retrieval and learned signals combined with explicit rules | Reasoning API |
| Continual Learning | Training tasks and state that change over time | Continual learning |
| SSM / Consolidation | Memory processing and consolidation controls | Memory API |
| Self-Improvement | Inspection of proposed changes and learning state | Learning isolation |
| SNN Networks | Event-based neural network operations | Neural systems |
| ML Models | Model lifecycle and inference | Models |
| MHN Patterns | Associative storage and pattern recall | Associative memory |
Verify the operation, not just the status label
Read a job's input, configuration, state, and resulting artifact. A completed label is insufficient when the handler is still a placeholder. The current source has acknowledgment-only continual checkpoint operations and a BPTT training path without an executed training loop. These limitations are explicit in instance coverage.
For experimentation, use a test dataset and a measurable outcome. Preserve the version of the model, inputs, and configuration so you can compare results or return to a known state through a verified recovery path.