Rationale

Why Separate From dag-ml

Data compatibility changes faster than graph execution. New modalities, axes, containers and adapters should not require changes to the ML execution core.

The separation also limits leakage risk: dag-ml can enforce fold and OOF rules without inspecting raw features.

Why Semantic Axes

A dimension is not just a number. A model input depends on whether an axis is a sample, wavelength, channel, time, token or graph node axis. Semantic axes make representation compatibility explicit and testable.

Why Deterministic Fingerprints

Predict/replay needs to know whether a new dataset schema is compatible with the schema used at fit time. Fingerprints provide a stable comparison key for bundle loading and cross-language conformance.

Non-Goals

• no execution graph;

• no cross-validation or OOF logic;

• no model fitting;

• no domain-specific defaults in the core;

• no assumption that every payload is a dense matrix.