ActionCipher / README.md
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---
pretty_name: ActionCipher
task_categories:
- visual-question-answering
tags:
- visual-in-context-learning
- compositional-reasoning
- inverse-planning
- synthetic
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: discovery
path: data/discovery-*
- split: validation
path: data/validation-*
- split: heldout_test
path: data/heldout_test-*
---
# ActionCipher
ActionCipher tests whether a vision-language model can infer an episode-specific
symbol-to-action mapping from five visual transition demonstrations and then emit
the shortest symbol sequence that transforms a query start pose into its goal.
## Dataset
- 3 grid sizes: 3 by 3, 4 by 4, and 5 by 5
- shortest plan length `h`: 1, 2, or 3
- 50 independent problems for every `grid_size` and `h` combination
- 450 independent problems total
- 2 symbol mappings per problem, giving 900 rows
- 12 separate model-input images plus one overview audit image per row
- one unique shortest answer per row
- 26 columns; internal renderer, pose, and duplicated audit columns are omitted
| Split | Independent problems | Rows |
|---|---:|---:|
| discovery | 225 | 450 |
| validation | 90 | 180 |
| heldout_test | 135 | 270 |
| **Total** | **450** | **900** |
## Row structure
Each row contains five demonstrations. A demonstration consists of separate
`before` and `after` images plus its symbol label. The query consists of separate
`start` and `goal` images. The model should return `answer` and nothing else.
`codebook`, `primitive_plan`, and `answer` are gold fields for scoring and
analysis. The `overview` image also contains the gold answer in its title. These gold
fields and the overview must not be inserted into the normal model prompt.
## Prompt
```text
Infer what each symbol means from the demonstrations.
Apply symbols from left to right.
For the query, return exactly one line containing only the shortest valid symbol sequence.
Put one space between symbols. Stop immediately after the last required symbol;
do not explain and do not repeat symbols after the answer is complete.
```
Present `demo_1_before`, `demo_1_after`, then `demo_1_label`, and repeat through
demo 5. Finally present `query_start`, `query_goal`, and ask for the answer.
## Load
```python
from datasets import load_dataset
dataset = load_dataset("Hanoi0126/ActionCipher")
```
See [COLUMN_REFERENCE.md](COLUMN_REFERENCE.md) for the exact 26-column schema.