Public execution matrix

This matrix describes the current multimodal development candidate. The new profiles below await the complete-phase review and installed-package test run. Existing test files and earlier release results are not proof that this candidate passes. Its qualification must cover integrations, official examples and fresh process archive replay with matching DAG-ML, IO, Methods and Core builds.

Existing means an established public route with regression witnesses. New means source and witnesses have been written in this phase. Both require the final qualification. Refused identifies a combination outside the stated profile; it is not retried through the legacy engine. Each row is bounded by its own contract: support for two rows does not imply support for their combination.

Python training and replay

Unless stated otherwise, these routes use nirs4all.run(..., engine="dag-ml"). Python estimator archives require a trusted producer and the recorded Python dependencies. They do not become R/WASM models by using the .n4a extension.

Public journey	Code status	Supported contract and explicit boundary	Regression or new integration witnesses
Simple NIRS regression, CV or full training	Existing	Dense array/dict/directory inputs, fold-local transforms, native scores and captured replay. Explicit engine selection stays strict.	test_general_dag_run.py, test_full_train_dag_run.py, test_general_dag_replay.py
Parallel branches and concatenated features	Existing	Declared branches and source layout; export retains the fitted transforms and exact concatenation order. Unconsumed merge options and ambiguous flat replay are refused.	test_source_concat_public.py, test_named_dag_stacking.py
Complete multisource early/intermediate fusion	Existing	Typed signal/image/series/metadata sources keep their dimensions until each encoder. Train/Test and groups remain explicit; unknown IDs, changed units/axes or incompatible dimensions fail.	test_multimodal_dagml.py, test_multimodal_targets.py
Operator/parameter generators	Existing; new by_source capture route	Native enumeration and selection. Captured by_source operator choices preserve per-source producer identities. Mixing operator and parameter generation in that new capture route is refused.	test_attested_by_source_packages.py; generator parity cases
Ranked by_source selection, Python or CLI	New	Original native ranking, a signed requested rank and a real CV/REFIT capture for each rank. Repeated CV work is intentional. Dynamic HPO-study top-k is refused.	test_attested_by_source_packages.py
Feature/sample augmentation on supported dense pipelines	Existing	Train-only augmentation and repetition/origin lineage; feature extend, add and replace retain their replay layout. This does not enable augmentation of named Torch inputs or partial-source late stacks.	test_repeated_feature_augmentation.py, test_dagml_cv_augmented_train_view.py, test_dagml_full_train_augmentation.py
Prediction stacking	Existing	Native inner OOF joins and learned meta-model, with genuine group/fold scopes. Training predictions cannot silently replace OOF features.	test_source_stacking_native.py, test_resampled_dag_stacking.py, test_dagml_shared_stacking.py
Target transformations	Existing	Target transforms remain fold-local and captured for replay on supported complete-data routes. Separate target-transform steps are refused by the new partial late and experimental-unit profiles.	test_general_dag_run.py, test_residual_target_preprocessing.py
Partial-source early/intermediate fusion	Existing	Explicit zero_with_indicator, per-source presence and optional per_target regression. Encoders fit only present/observed rows; target values are never imputed.	test_multimodal_missing_sources.py, test_multimodal_partial_targets.py
Partial-source late regression/classification	New	Two to four branches, true inner OOF, one learned fusion model, global stack HPO, complete mono-target classification or per-target regression. Classes must remain available in each fitting scope. Branch-local HPO, mixed task types and partial class labels are refused.	test_multimodal_late_partial.py, test_incomplete_source_stacking_execution.py
Upstream preprocessing before partial by_source	New	Ordinary reconstructible X transforms are cloned per source and fitted inside each native source/target training intersection. Whole-stack HPO addresses the expanded chain; selected REFIT and replay apply it once. Global-fit/transfer/structured operations stay refused. Experimental units require explicit weighted support in every upstream and branch component before compilation/ask.	test_incomplete_source_stacking_upstream.py; test_source_stacking_upstream.py (unit)
Late archive capture and cold prediction	New	Exact REFIT components, masks, vocabularies and per-target row identities; target-free prediction can omit an entire declared source. Original native artifact identities and serialized-file hashes are checked separately before unpickling.	test_multimodal_late_missing_export.py, test_attested_by_source_packages.py
Ragged series via SequenceSummary	Existing; new domain constraints	Packed variable lengths, fixed channels; explicit minimum length and inclusive channel bounds are checked at fit and predict. No implicit interpolation, clipping or padding; time coordinates do not weight the summary.	test_multimodal_series_domain.py; official U12
Independent experimental units	New	Explicit unit/repetition identities, native scope-local equal-unit weights and unit-level scores, while OOF/predictions stay observation-level. Weighted sklearn components are required; no automatic substitution of split groups.	test_multimodal_experimental_units.py
Joint named Torch fusion	New	CPU, two to four complete numeric fixed-shape sources, raw axes passed to user forward(**inputs), one complete numeric target, Adam/MSE, native CV/REFIT and Python replay. Ragged joint tensors, missing modalities, mixed metadata, other frameworks and portable Torch weights are outside this profile.	test_named_torch_training.py, test_named_torch_nd.py
Finite data provider	Existing	The provider supplies a bounded dataset/view under the native run/fold lifecycle. Prediction replay does not regenerate the training data. Infinite streams and checkpointed mid-epoch continuation are separate work.	test_data_provider.py, test_data_provider_views_probe.py; official U11/U12

The named files live under tests/integration/api/, except the augmentation and residual-target parity witnesses under tests/integration/parity/. The multimodal guide gives input and model syntax; partial late fusion describes its mask and archive rules. See pipeline keywords for the established generator, branch and model-local tuning syntax.

Native portability

Public journey	Code status	Boundary
Methods Archive V2 and four-modality U07 replay	Existing	Real exported Methods states, native DAG scheduling and exact byte identities. Python/R/WASM role adapters have bounded supported recipes.
Native full-refit Archive V3 with N4ME states	New	A genuine new REFIT produces V3; DAG owns semantics and Core owns ZIP/inventory validation. NativeMethodsRefitResult.from_package_json, export, load_archive and predict expose the fitted child in Python. This does not add every N4ME method to nirs4all.run(engine="native").
Native PLS RolePipeline in V3	New	The persisted native controller and its selected recipe survive export/replay. Host Python/R wrappers cannot impersonate this controller.
Autonomous R ZIP replay	New	Public R APIs call native Core ZIP validation and DAG replay without Python. Direct Methods replay covers native Role V2/V3 and N4ME V3; the separate process adapter covers the explicit U07 recipe in V2. A process V3 route requires a genuine compatible producer and is not qualified by the direct Methods witnesses.
Web full-refit and prediction replay	New	Native DAG initial full-refit and replay operations schedule the browser controller. The fitted browser pipeline is an explicit JSON sidecar, not a native RAW model. Existing cross-validation preprocessing/branches remain host-controlled on native folds.
Web grouped folds and observation lineage	New	Explicit group/origin/repetition/augmentation roles pass from input metadata to DAG data. Native GroupKFold uses declared groups; missing identities or unsupported combinations are refused.
Torch/joblib in R or WASM, arbitrary mixed-language nodes	Refused	Deferred portability profiles; no conversion or host-runtime fallback is implied.

The candidate’s release checks must exercise independent numerical oracles, unequal repetitions and leakage refusals, exact input-contract refusals, and fresh-process replay with FIT/HPO disabled. Small deterministic multimodal datasets used for these checks are test fixtures. The existing NIRS synthetic generator remains a separate product feature.