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Architecture and Reduction

This section explains how Radixor turns textual dictionary input into a compact compiled stemmer and how reduction affects the semantics preserved in the final runtime artifact.

Radixor is easiest to understand when separated into two related concerns:

  • architecture: what structures exist, how data moves through them, and what runtime lookup actually does,
  • reduction semantics: what it means for two subtrees to be considered equivalent and how that choice affects get() and getAll() behavior.

The short version

Radixor does not keep a large flat table of final stems. Instead, it converts dictionary entries into patch commands, stores them in a trie, reduces equivalent subtrees, and freezes the result into an immutable compiled structure.

The build-time flow is:

flowchart TD
    dictionary[Training dictionary]
    mutable[Mutable trie]
    reduced[Reduced trie]
    compiled[Compiled trie]

    dictionary --> mutable --> reduced --> compiled

Both implementations follow this conceptual flow. Java materializes its object-based compiled trie and exposes multiple reduction modes; the Python extension implements the production dominant-result profile in Rust and stores the runtime trie in flat arrays. Their persisted interoperability boundary is the version 7 binary stream, not their in-memory representation.

For registered Java models, the dictionary is an independently versioned GZip resource discovered through a descriptor and verified before this flow begins. For Python's standard models, this flow runs during package preparation and the installed radixor-models-standard distribution already contains validated compiled version 7 tries. See Model Selection and Loading for Java discovery and Architecture for component and release boundaries.

Explicit descriptors and stable model IDs now use the same compiled-value path as language defaults. loadCompiled(descriptor, ...) and loadCompiled(modelId, ...) first build with serialized patch commands and then map those values to CompiledPatchCommand while preserving metadata, reduction semantics, and ranked getAll order. Very large inputs can have a high temporary construction peak; PoliMorf is verified in an isolated 6 GiB JVM rather than increasing ordinary test or Gradle daemon heaps.

At runtime, the compiled trie does not directly return the final stem string. It returns one or more stored patch commands for the addressed key, and those commands are then applied to the original input word.

Why this matters

This design gives Radixor several practical properties at once:

  • compact deployable artifacts,
  • deterministic runtime behavior,
  • support for both preferred and multiple candidate results,
  • separation of preparation-time complexity from runtime lookup.

It also explains why a large source dictionary can be transformed into a much smaller compiled artifact without discarding the operational behavior that matters to the caller.

Reading guide

Use the following pages depending on what you need to understand:

  • Architecture explains the data flow, core structures, patch-command lookup model, and why the compiled trie is efficient at runtime.
  • Reduction Semantics explains how subtree equivalence is defined, what ranked, unordered, and dominant reduction preserve, and how those choices affect observable lookup behavior.

For most readers, the best order is:

  1. Architecture
  2. Reduction Semantics