Capability Matrix

The long-term target is to replace the current nirs4all core pipeline engine with a lower-level, reproducible OOF campaign engine. The matrix below is the scope guard: every capability must either be represented in core contracts or explicitly delegated to dag-ml-data/controllers without weakening OOF and leakage guarantees.

Pipeline Surface

Capability	dag-ml responsibility	dag-ml-data / controller responsibility	OOF / leakage invariant
Multisource	graph/source join nodes, phase control	source descriptors, alignment, presence masks, fusion plans	sample ids are canonical across sources; missing-source policy is explicit
Repetitions	split unit, aggregation decisions and custom aggregation-controller task/result validation	SampleRelation observation/sample/target mapping; optional external custom reducers	no observation from the same leakage unit crosses train/validation, and custom aggregation outputs must preserve requested sample/unit coverage
Grouped samples	group-aware split validation	expose group ids in sample relations	group id cannot appear in both train and validation for a fold
Augmentation	train-only phase gating, origin checks	expose origin_id, augmentation adapter declarations	validation origins cannot be augmented into train leakage or vice versa
Processings	node lineage and fit scope	representation adapters and fitted adapter refs	stateful processing fits only on fold train during CV
Splitters	identity fold generation, validation and canonical fold fingerprints	group/origin/sample identity inputs	folds are sample-id based, deterministic and replayable
Models	controller ABI, fit/predict phase ordering	host controller implementation	downstream training may consume only validation OOF predictions by default
Refit	selected graph replay and final-fit phase	replay data plans and fitted adapter refs	refit artifacts cannot be used to manufacture training meta-features
Branching	fork/map/subgraph semantics	branch-local views and materialization	branch outputs preserve lineage and fold identity
Merging	feature/prediction/source join nodes	feature/source alignment and concatenation	prediction merges validate OOF; feature/source merges validate identity alignment
Concatenation	declare merge intent and downstream contracts	feature joiner and namespace policy	row order is canonical by sample id; no positional join
Finetuning	phase/fold control and leakage flags	stateful controller/adapter fit implementation	any learned transform/model is fitted on fold train only during CV
Generation	search-space expansion, variant fingerprints, typed node-param override lowering	adapter/model params as serializable JSON	each variant has deterministic seeds, fingerprints, effective params and lineage
Tuning	tuner node phase control and nested split policy	tuner/controller execution	tuner observations respect nested CV boundaries
Controller multitask	explicit task-group templates, batch admission, fallback and per-member validation	fused host/GPU execution for known task groups or closed static subgraphs	batching cannot hide topology, mix fold partitions, or replace per-node lineage/results
Prediction replay	bundle validation and phase restrictions	schema fingerprint and data plan replay	predict never reuses CV validation labels/features in training mode
Explainability	replay hooks and opaque outputs	controller-specific explanations	explanation payloads do not alter fit/predict lineage

Non-Negotiable Rules

1. All train/validation/test/final semantics are keyed by stable sample ids.

2. Fold construction and OOF joins must never use row position as identity.

3. Stateful processing, finetuning and supervised adapters are fitted inside the current training boundary only.

4. Refit artifacts are final inference artifacts, not meta-training features.

5. Any unsafe train-prediction-as-feature path must be explicit, searchable and permanently marked in lineage.

6. Generated variants carry deterministic fingerprints, seed contexts and parameter choices.

7. dag-ml-data may describe relations and fit scopes, but dag-ml enforces ML invariants.

8. A training-phase edge marked requires_oof must be backed by validation predictions in the core PredictionStore; raw upstream handles are not forwarded across that edge.

9. A multitask controller batch is accepted only if every member task is valid on its own and every member result is returned and validated separately.

MVP To Full Replacement Path

Stage	Coverage
MVP	UC6 stacking success and UC11 train-prediction refusal
Next	group-aware folds, source alignment, stateful processing fit scopes
Replacement spike	multisource + repetitions + augmentation + branch/merge from current nirs4all fixtures
Hardening	generated variants, nested tuning, refit/replay bundles, process/thread scheduling