Saltar al contenido principal

lambda

Since v1.10.0 · Top-level declaration

Grammar​

lambda <Name> {
    ontology: "<string>"                                # optional — ontological class
    certainty: <0.0..1.0>                                # optional — confidence (default 1.0)
    temporal_frame: "<start>" ["<end>"]                  # optional — validity window
    provenance: "<string>"                               # optional — source provenance
    derivation: <raw|inferred|derived|aggregated|transformed>  # optional — derivation kind
}

# Inside a flow body — application form:
step <Name> {
    ...
    lambda <Name> on <target> -> <OutputType>
    ...
}

lambda declares typed lambda-data — a value with explicit provenance, certainty, temporal frame, and derivation status. Where type declares structural shape and axonstore declares persistent rows, lambda declares epistemic metadata for a derived value: what it claims, how confident, when valid, where it came from, how it was derived.

The companion flow-step form lambda <Name> on <target> -> <Out> (the "lambda apply" pattern, v1.10.0) applies the declared metadata to a step's output — producing a typed, audit-traceable derived value.

Surface​

lambda is a top-level declaration. The flow-step application is a separate parser path (parse_lambda_data_apply) that lives inside a flow body.

Top-level declaration​

lambda DiagnosisCandidate {
    ontology:       "ClinicalInference"
    certainty:      0.85
    temporal_frame: "2025-01-01" "2026-12-31"
    provenance:     "EHR cohort 2024 + clinical guideline ICD-11"
    derivation:     inferred
}

Flow-step application​

flow DiagnoseSymptoms(symptoms: SymptomList) -> Diagnosis {
    step Cluster {
        given: symptoms
        ask: "Cluster the symptoms."
        output: ClusteredSymptoms
    }
    step Decide {
        given: Cluster.output
        lambda DiagnosisCandidate on Cluster.output -> Diagnosis
        ask: "Emit the diagnosis."
        output: Diagnosis
    }
}

Fields​

ontology: (optional)​

A string literal declaring the ontological class the value claims membership in. Examples: "ClinicalInference", "FinancialPrediction", "LegalOpinion". The runtime carries this verbatim into the audit row; downstream consumers can filter / route by ontology.

certainty: (optional, defaults to 1.0)​

A numeric literal in [0.0, 1.0]. The declared confidence of the value. Pairs with the persona's confidence_threshold: and the anchor's confidence_floor:: runtime checks the chain.

temporal_frame: (optional)​

One or two string literals declaring the validity window.

FormMeaning
"<start>"Open-ended frame starting at the named date/time.
"<start>" "<end>"Closed frame between start and end.

The runtime treats values used outside their declared frame as Uncertainty per the epistemic lattice (v1.4.0).

provenance: (optional)​

A string literal documenting the source provenance — where the data came from, who curated it, what version. Free-form; appears verbatim in audit rows for human review.

derivation: (optional)​

A single identifier from the closed derivation catalogue (axon-frontend::type_checker::VALID_DERIVATIONS):

ValueMeaning
rawDirect measurement / unprocessed input.
inferredOutput of a cognitive inference (LLM, classifier).
derivedComputed from other values via deterministic rules.
aggregatedSummed / averaged over multiple inputs.
transformedRe-shaped from another representation (encoding shift).

The lambda apply flow-step form​

The v1.10.0 application pattern attaches a top-level lambda declaration to a specific step's output. The grammar is:

lambda <LambdaName> on <TargetStep>.output -> <OutputType>

The runtime stamps the lambda's metadata (ontology, certainty, temporal_frame, provenance, derivation) onto the target's output as it flows downstream. Audit row: lambda:<name>:applied_to:<target>:<output_type>.

What this primitive is NOT​

  • Not a function in the Church / λ-calculus sense. The name is borrowed for the "derived-value metadata" surface; there is no first-class function abstraction at this layer. For function-like composition, use flow + apply.
  • Not a type. type declares structural shape; lambda declares epistemic metadata about an instance.
  • Not a compute. Compute pins the backend that produces the value; lambda annotates the produced value with provenance.
  • Not optional for high-stakes derivations. Production flows in regulated domains (clinical, financial, legal) declare lambda on every inferred output so the audit trail records (ontology, certainty, temporal_frame, provenance, derivation).

See also​

  • axon://primitives/type — structural-shape counterpart.
  • axon://primitives/anchor — confidence_floor: pairs with lambda's certainty:.
  • axon://primitives/compute — pins WHICH model produced the value the lambda annotates.
  • axon://compliance/gxp — examples of GxP section 21 CFR Part 11 audit-trail propagation through lambda metadata.