Representational form

Type: kb/types/definition.md · Tags: learning-theory, artifact-analysis

Representational form classifies how a retained artifact's operative part is encoded and consumed. This KB uses three coarse forms: natural-language, symbolic, and distributed-parametric. Mixed artifacts are split by operative part or consumption path when the parts have different review evidence, invalidation needs, or rollback paths.

Scope

Natural-language carries behavior-shaping content whose consequences come from interpretation by a language model or human. Prompts, reflections, notes, policies, playbooks, and many skills have natural-language operative parts where their force comes from interpretation.

Symbolic carries behavior-shaping content in localized units whose consequences are assigned by a parser, interpreter, runtime, schema, validator, route table, or other defined consumer. The category is architectural, not a claim about classical symbolic AI.

Distributed-parametric carries behavior-shaping content in numerical state distributed across parameters or dense representations: weights, adapters, embedding spaces, dense-vector indexes, reward models, learned controllers, and similar artifacts.

Form sets the default inspection method: read natural-language content, test or statically check symbolic artifacts, and probe distributed-parametric artifacts behaviorally.

Derivation

Two axes generate the categories. Assigned consequences — does a defined consumer fix what the artifact does, or is it reinterpreted on each consumption? A formal semantics is sufficient but not necessary: most programming languages have none, and still sit on this side because an implementation adjudicates, so two consumers disagreeing means one of them is wrong. Natural language has no such adjudicator — two readings can both be reasonable — which is underspecification, not a defect of any particular text. Localization — is there an identifiable unit carrying the content, or is it spread across numerical state with no addressable part?

localized non-localized
consequences assigned symbolic —
consequences not assigned natural-language distributed-parametric

The fourth cell is unoccupied in this domain rather than impossible. Vector symbolic architectures aim at exactly it, binding and bundling being algebraically defined operations over distributed hypervectors. The three-way carve therefore holds while agent systems retain nothing of that shape, and gains a fourth form if they do.

The inspection rule above follows from the axes instead of being stipulated per form: a localized unit with defined semantics can be checked against them (test), a localized unit without them yields its content only to interpretation (read), and a non-localized artifact offers nothing to point at, so only its behavior is observable (probe). Addressability rides on the localization axis alone, which is why a prompt and a validator are both revisable one item at a time while weights are not.

Because natural-language and symbolic share the localized side of the table, the pair is referred to jointly as the localized forms — the class name reads the localization axis off rather than adding vocabulary. "Readable artifacts" and "the readable pair" remain informal aliases; prefer the derived name where the carve matters.

Exclusions

Representational form is not storage substrate. Markdown in a repository can be natural-language, symbolic, or mixed depending on the consumer. A vector store can expose natural-language records while its retrieval behavior depends on distributed-parametric embeddings and ranking.

Localized is a claim about unit structure, not about locality of reference: it means an identifiable unit carries the content, not that the content is deployment-local. A fact indexed to one deployment — the "current local state" of the absorption discussion — can be retained in any form, including weights; the two senses are orthogonal and must not be read into each other.

Representational form is also not consumption path. Prompt is exact when material is supplied, or explicitly assembled to be supplied, as model input. A stored note, policy, or memory record is not thereby a prompt because it might later be retrieved; retain its precise artifact name, or call it natural-language when the representational category matters. A generated model-input view can be a prompt even when it assembles natural-language and symbolic operative parts.

Misuse Cases

  • Calling learned weights "opaque" as if opacity were the form. The form is distributed-parametric; opacity is a practical inspection property that also appears at sufficient scale in natural-language and symbolic systems.
  • Calling every YAML or Markdown artifact symbolic. It is symbolic only where a consumer assigns defined consequences to specific fields, values, or structures.

Revision rationale

The category was renamed from prose to natural-language because its boundary is interpretive, not editorial. Fragments, facts, reflections, structured records, rules, and prompt components can receive their consequences through natural-language interpretation without being continuous expository prose. The older label therefore excluded central cases by connotation and encouraged the storage package to stand in for the operative part.

Apply the vocabulary in this order: name the precise artifact when the category adds nothing; use prompt when model-input supply is the point; use a natural-language category term when representational form matters; preserve prose for editorial meaning, quotations, historical terminology, and named review machinery. This ordering keeps prompt narrower than natural-language artifact and prevents a stored artifact from being reclassified merely because one future consumption path may place it in context.

Relevant Notes: