Tensor
tensor@1 defines the dense numerical execution semantics of the Forge Pool execution platform.
Tensor workloads operate over structured numerical data including matrices, tensors, vectors, and dense computational representations.
The Tensor family extends the Forge Pool execution model toward numerical computing and AI-oriented workloads while preserving the same canonical execution contract, deterministic execution doctrine, replay model, and verification framework shared by all primitive families.
Status
tensor@1 is an emerging primitive family.
The execution semantics and family model are defined.
Public capability availability may expand through future platform releases.
The family is documented to establish the execution model and future extension path without changing the canonical execution contract.
Purpose
Tensor execution is designed for dense numerical workloads where computation operates over structured numeric representations.
Representative domains include:
- matrix operations
- tensor transformations
- vector computation
- embedding pipelines
- numerical preprocessing
- AI inference preparation
- AI inference post-processing
- distributed numeric reduction
The primitive family defines numerical execution semantics.
Execution profiles define domain-specific tensor operations.
Operation Identity
Tensor workloads are selected through the canonical execution contract.
{
"op": {
"name": "tensor",
"version": 1,
"profile": "matmul.v1"
}
}Tensor execution uses the same public execution endpoint:
POST /api/v0/ops/executeNo tensor-specific API surface is introduced.
Execution Characteristics
Tensor execution is characterized by:
- dense numerical computation
- structured data transformation
- deterministic numerical operations
- distributed execution-unit processing
- deterministic reduction
- replay-compatible execution evidence
The execution runtime may evolve independently from tensor semantics.
Representative Profiles
Representative Tensor profiles include:
matmul.v1
Future profiles may extend the Tensor family toward additional numerical and AI-oriented workloads while preserving the same execution contract.
Execution Parameters
Tensor profiles may define parameters such as:
- tensor dimensions
- numerical representation
- operation configuration
- precision settings
- transformation parameters
- reduction strategy
- artifact preferences
Each profile defines its own schema while inheriting the shared tensor@1 execution semantics.
Deterministic Execution
Tensor execution follows the same deterministic execution guarantees as every Forge Pool primitive family.
Equivalent execution contracts must preserve equivalent:
- numerical operations
- precision behavior
- reduction semantics
- replay metadata
- execution evidence
Infrastructure placement, runtime topology, and execution routing must not change computational meaning.
Reduction Semantics
Distributed tensor execution may produce intermediate numerical results.
Reduction combines those results into canonical outputs.
Reduction may produce:
- result tensors
- matrices
- numerical summaries
- precision metadata
- verification summaries
- workload-specific outputs
Reduction remains deterministic, versioned, and replay-compatible.
Execution Evidence
Tensor execution may produce:
- numerical metadata
- shape information
- precision information
- execution metrics
- artifact references
- replay metadata
- verification outcomes
Execution evidence allows numerical workloads to remain inspectable and reproducible.
Verification
Tensor verification confirms that numerical execution followed the declared execution contract.
Verification may inspect:
- operation identity
- numerical configuration
- precision metadata
- artifact integrity
- reduction behavior
- replay metadata
Verification validates execution semantics rather than hardware-specific implementation details.
Replay
Replay preserves the computational meaning of tensor execution.
Replay depends upon:
- operation identity
- tensor profile
- canonical parameters
- numerical semantics
- reduction behavior
- execution evidence
Equivalent tensor execution contracts must preserve equivalent numerical meaning.
See:
Relationship to Other Primitive Families
Tensor extends the Forge Pool execution taxonomy with dense numerical computation.
Unlike:
mc@1, which specializes in probabilistic simulationsearch@1, which specializes in retrieval and rankinggraph@1, which specializes in relationship computationensemble@1, which specializes in deterministic compositionmedia@1, which specializes in transformation workflows
tensor@1 specializes in structured numerical execution.
All primitive families share the same execution doctrine.
Relationship to AI Workloads
Tensor provides computational building blocks that can support AI-oriented workflows.
However, Tensor is not limited to AI.
The primitive family represents a broader numerical execution category that can support future workloads requiring dense computation.
Relationship to Examples
As public Tensor capabilities become available, representative workloads will be documented through the Examples section.
Examples will demonstrate capability behavior.
This document defines primitive-family execution semantics.
Related Documentation
Continue with:
Continue in Forge Studio
To explore Tensor capabilities as they become available:
- Browse available capabilities in Capability Explorer
- Inspect execution blocks in Block Registry
- Execute supported numerical workloads
- Review execution evidence and replay metadata
- Validate deterministic execution behavior
Trust should be established through independent verification rather than documentation alone.
Final Note
Tensor is not a separate execution system.
It is an extension of the Forge Pool primitive family model.
By introducing dense numerical execution through the same canonical execution contract, Forge Pool preserves one unified execution doctrine across simulation, retrieval, graph computation, media transformation, ensemble composition, and future numerical workloads.