# Downstream Graph-WAM Instructions

The next student should treat this dataset as graph-augmented robot trajectory data for DreamZero-style joint vision+action flow matching.

## Do not assume graph conditioning is the main path

The graph is not assumed to be the main conditioning input. It can be used in several ways, in this recommended order:

1. Structured modality/state stream:

```text
history RGB + robot state + language + graph state -> joint future vision/action model
```

2. Auxiliary ΔG target:

```text
predict future video/action plus relation/event changes
```

3. Joint denoised graph modality:

```text
flow match [future video latent, future action, future graph state]
```

4. Conditioning ablation only:

```text
same model with graph tokens as extra conditioning
```

The dataset intentionally supports all of these.

## Default chunk builder

For chunk starting at `t`:

```python
context_rgb = frames[t-L:t]
context_state = robot_state[t-L:t]
context_graph = graph_tensors[t-L:t]
target_rgb = frames[t:t+H]
target_action = action[t:t+H]
target_graph_delta = events_or_transition_masks[t:t+H]
language = episode.language_instruction
```

## Required ablations

- no graph;
- graph shuffled across episodes;
- stale graph from earlier timestep;
- state-tier only;
- prior-tier only;
- graph as auxiliary target only;
- graph as conditioning stream;
- graph as joint denoised modality;
- right-only vs left-only vs right+wrist vs all three views.

## Metrics

Use both visual/action metrics and graph-state metrics:

- action MSE / control accuracy / rollout success;
- video reconstruction/prediction metrics;
- relation event F1;
- contact/support/articulation ΔG accuracy;
- target-object visibility/ROI metrics;
- corruption sensitivity.