# GNN4Colliders architecture

## Current architecture

GNN4Colliders is organized around an architecture-neutral event boundary:

```text
ROOT/Awkward
    ↓
EventSample + EventMetadata
    ↓
shared features and representation adapters
    ├── GraphSample -> GraphSampleCache -> GraphBatch
    └── SequenceSample -> transformer/token models
    ↓
model family
    ↓
Task -> Trainer/Predictor -> named outputs
```

| Layer | Responsibility |
|---|---|
| `data` | ROOT/Awkward ingestion, metadata, datasets, folds, batching, and caches |
| `features` | Collider-object features and derived physics quantities |
| `graphs` | Topology, edge construction, and graph-specific adapters |
| `models` | Architecture-specific neural networks |
| `tasks` | Labels, weights, losses, scores, predictions, and metrics |
| `training` | Optimizers, lifecycle, reproducibility, checkpointing, and distributed utilities |
| `inference` | Ordered prediction, evaluation, and named output writing |
| `cli` / `config` | Thin semantic entry points and configuration composition |

Production code must depend on these package boundaries rather than on
historical implementation paths. The canonical ROOT-GNN implementation is
`gnn4colliders.models.root_gnn.EdgeNetwork` with
`FineTunedEdgeNetwork` as its transfer boundary. New architecture families
must reuse shared data, feature, task, training, and inference interfaces where
their representation permits it.

## Data and representation boundaries

`EventSample` and named `EventMetadata(fold, weight, sample_id, extra)` are
representation-independent. `GraphSample` and `GraphBatch` are the current
graph representation boundary. `TensorGraph` provides a native tensor path
for ROOT-GNN execution on CPU, CUDA, and MPS; DGL remains an optional graph
adapter and cache dependency.

`SequenceSample` and `SequenceBatch` are the topology-free representation
boundary for transformer experiments. `TransformerClassifier` consumes padded
object-token sequences and returns raw logits while reusing shared task,
training, checkpoint, and inference interfaces.

The active collider node schema has seven columns:
`[pt, eta, phi, energy, btag, charge, node_type]`. Graph edges are directed,
source-major, fully connected without self-loops except for the one-node
case, and carry `[deta, dphi, dR]` features. These are compatibility contracts
captured by deterministic tests and the frozen `root-gnn-parity-baseline`
reference fixture.

## Configuration and lifecycle

Experiments use semantic configuration such as `model.type: root_gnn`; model
module paths are not part of the new public configuration contract. The CLI
delegates to tested application factories and does not contain model or data
processing logic.

Checkpoints carry model/task metadata, lifecycle state, schema versions, and
optional RNG state. The compatibility package accepts historical checkpoint
prefixes and classifier names as a one-way input adapter. No executable
historical model code is required at runtime.

## Extending the model families

Additional model families should introduce only their representation-specific
boundary and model implementation, for example:

```text
data.EventSample -> features -> SequenceSample -> models.root_transformer
```

It should include a deterministic fixture, unit tests for the representation,
an integration path through the shared task/trainer interfaces, and explicit
checkpoint/inference behavior. Shared infrastructure should be generalized
only when the second model demonstrates a real common use case.

## Frozen baseline

The complete ROOT-GNN parity campaign is recorded by the
`root-gnn-parity-baseline` tag. The historical implementation is no longer
part of the active source tree. Reference data and compatibility adapters are
kept so existing checkpoints and scientific observations remain usable while
development moves to new architectures.