Model backends

Scoring is behind a two-method ModelBackend SPI. Two implementations ship:

HistoryBaselineBackend (default)

Model-free: evaluates the query target over the entity's own trailing history windows ("self labels") and aggregates. Transparent, deterministic, zero artifacts — the whole pipeline runs and tests without a model. Use it for development, testing, and as a sanity floor for model quality.

RtNativeBackend

Scores contexts with real RT-J checkpoints through the native C++ engine (librt_c). It converts each context into the raw RT token batch — one token per feature cell, FK links as the node graph, per-column z-scores for numbers, pinned MiniLM embeddings (384-dim) for text cells and "<column> of <table>" schema phrases — plus a masked task row anchored at prediction time, with the entity's own past outcomes as in-context examples.

Classification logits pass through a sigmoid; regression outputs denormalize with in-context label statistics.

Checkpoint routing

ModelConfig maps the inferred task type to a checkpoint URI:

Task type	Default URI
classification, ranking	hf://stanford-star/rt-j/classification
regression, forecasting	hf://stanford-star/rt-j/regression
text embeddings	all-MiniLM-L12-v2 (pinned, 384-dim)

hf:// URIs resolve against the local Hugging Face cache only — nothing downloads implicitly. file:// and plain paths also work.

Bring your own

Implement ModelBackend to plug in any scorer; the engine hands you assembled contexts and the routed model URI.