task_id string | train_demonstrations_json string | test_input_json string | test_output_json string | stable_instances_50_json string | generator_py string | verifier_py string |
|---|---|---|---|---|---|---|
t1 | [{"input":[[0,0,3,0,0,0,0,2,0,0],[0,3,0,0,0,0,0,0,2,0],[3,0,0,0,0,0,0,0,0,2],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[6,0,0,0,0,0,0,0,0,1],[0,6,0,0,0,0,0,0,1,0],[0,0,6,0,0,0,0,1,0,0]],"output":[[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,3,2,0,0,0,0],[0,0,0,3,0,0... | [[1,0,0,0,0,0,0,0,0,0,0,2],[0,1,0,1,0,0,0,0,0,0,2,0],[0,0,1,0,0,0,0,0,0,2,0,0],[0,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0],[0,8,0,0,0,0,0,0,0,0,4,0],[0,0,8,0,0,0,0,0,0,4,0,0],[0,8,0,8,0,0,0,0,4,0,0,0],[8,0,0,0,0,0,0,0,0,0,0,0]] | [[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,1,0,0,0,0,0,0,2,0,0],[0,0,0,1,0,1,0,0,2,0,0,0],[0,0,0,0,1,0,0,2,0,0,0,0],[0,0,0,1,0,0,0,0,0,0,0,0],[0,0,0,8,0,0,0,0,4,0,0,0],[0,0,0,0,8,0,0,4,0,0,0,0],[0,0,0,8,0,8,4,0,0,0,0,0],[0,0,8,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0]] | {"train":[{"input":[[0,0,0,0,0,0,0,0,0,0,0,0,5,0],[0,0,9,0,0,0,0,0,0,0,0,0,0,5],[0,0,9,0,0,0,0,0,0,0,0,0,0,0],[9,9,9,9,0,0,0,0,0,0,0,0,0,0],[0,0,0,9,9,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0],... | """Corner-components to center-components generator for Test2/t1.
The rule matches ``Test2/t1.json``:
- up to four components, one per corner window
- each corner component is translated to the corresponding center window
"""
from __future__ import annotations
import random
from typing import Dict, List, Sequence, T... | """Verifier for the Test2/t1 corner-to-center rule."""
from __future__ import annotations
from typing import Dict, List, Tuple
Grid = List[List[int]]
def _empty(size: int) -> Grid:
return [[0 for _ in range(size)] for _ in range(size)]
def _corner_span(size: int) -> int:
return size // 2 - 2
def _origi... |
t2 | [{"input":[[0,0,0,0,0,0,0,0,0,0],[0,0,1,0,0,0,0,8,8,8],[0,1,0,0,0,0,0,0,0,8],[0,0,1,0,0,0,0,0,0,8],[0,1,0,0,0,0,0,0,0,8],[0,0,1,0,0,0,0,8,8,8],[0,0,0,0,5,5,0,0,0,0],[0,0,0,0,5,5,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0]],"output":[[0,0,0,0,0,0,0,0,0,0],[0,0,8,0,0,0,0,5,5,5],[0,8,0,0,0,0,0,0,0,5],[0,0,8,0,0,0... | [[0,0,9,0,0,0,0,0,0,0],[0,9,9,0,0,0,5,0,0,0],[9,9,9,0,8,0,0,5,0,0],[0,0,0,8,0,4,0,0,5,0],[0,0,8,0,0,4,0,0,0,5],[0,8,0,4,4,4,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0]] | [[0,0,5,0,0,0,0,0,0,0],[0,5,5,0,0,0,4,0,0,0],[5,5,5,0,9,0,0,4,0,0],[0,0,0,9,0,8,0,0,4,0],[0,0,9,0,0,8,0,0,0,4],[0,9,0,8,8,8,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0]] | {"train":[{"input":[[0,0,0,6,6,6,6,0,0,0,0,0,0,0],[0,0,0,6,0,0,0,6,0,0,0,0,0,5],[0,0,0,0,0,0,0,0,0,0,0,0,5,0],[0,0,0,0,0,0,0,0,0,0,4,0,0,0],[0,0,0,0,0,0,0,0,0,4,0,0,0,0],[0,0,0,0,0,0,0,0,0,4,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,8,0,0,0,9,0,0,0,0,0,0,0,0],[0,8,0,0,9,0,0,0,0,0,0,0,0,0],[0,0,8,0,9,0,0,0,0,0,0,0,0,0],... | """Generator for Test2/t2: cyclic color shift by leftmost-object order.
Rule:
- detect 8-connected non-zero objects
- sort objects by the position of their leftmost cell (col, then row)
- shift object colors to the right with wrap-around
"""
from __future__ import annotations
import random
from typing import Dict, L... | """Verifier for Test2/t2 color-shift-by-leftmost-object rule."""
from __future__ import annotations
from typing import List, Sequence, Tuple
Grid = List[List[int]]
Cell = Tuple[int, int]
def _neighbors8(r: int, c: int) -> list[Cell]:
out: list[Cell] = []
for dr in (-1, 0, 1):
for dc in (-1, 0, 1):
... |
t3 | [{"input":[[2,0,5,5,7,7],[2,2,2,3,7,4],[1,2,2,7,7,4],[1,2,3,0,7,0],[2,2,4,4,7,7],[2,2,4,4,0,0]],"output":[[4,1],[4,1],[0,2]]},{"input":[[0,0,8,0,3,0,3,0,0],[8,8,8,0,5,0,0,4,4],[0,6,8,6,5,6,4,0,5],[1,0,8,0,5,4,4,2,0],[0,1,8,3,0,5,0,0,2],[8,8,8,3,0,0,0,9,9],[0,0,3,2,2,2,2,9,0],[0,3,0,2,2,2,2,9,0],[3,3,0,2,2,2,2,9,5],[0,0... | [[5,5,0,0,0,0,0,0,0,7,7],[0,5,5,5,2,2,0,0,0,7,7],[0,0,5,5,0,2,2,0,1,7,3],[0,0,5,5,2,0,2,1,0,7,0],[0,0,0,0,0,0,1,0,0,7,7],[4,4,4,0,0,1,2,5,0,0,0],[0,0,4,0,1,0,2,0,5,0,0],[3,0,4,1,0,0,2,0,0,5,0],[0,3,4,0,0,0,2,2,2,0,5],[4,4,4,0,0,0,0,0,0,0,0]] | [[7,0,0],[3,3,0],[0,0,3]] | {"train":[{"input":[[9,5,8,9,4,5,9,0,0,0,0,0,0,0],[9,9,9,9,9,9,9,0,0,1,1,1,0,0],[0,0,0,0,0,0,0,0,0,1,1,0,0,0],[3,0,3,3,0,0,0,0,0,1,0,0,0,0],[0,3,3,3,0,0,0,0,0,0,0,0,0,0],[0,0,3,0,3,0,0,0,0,0,0,0,0,0],[0,0,3,0,0,0,0,0,0,0,0,0,0,0],[8,0,8,0,0,0,0,0,0,0,8,0,0,0],[8,8,8,0,0,0,3,3,3,0,8,8,8,8],[0,0,8,8,8,0,0,5,5,5,5,5,5,5],... | """Generator for Test2/t3: extract wrapped rectangle interior."""
from __future__ import annotations
import random
from typing import List
Grid = List[List[int]]
def _empty(h: int, w: int) -> Grid:
return [[0 for _ in range(w)] for _ in range(h)]
def _random_content(rng: random.Random, h: int, w: int, frame_... | """Verifier for Test2/t3 wrapped-rectangle task."""
from __future__ import annotations
from typing import List
Grid = List[List[int]]
def verify_t3(inp: Grid) -> Grid:
if not inp or not inp[0]:
raise ValueError("input grid must be non-empty")
h = len(inp)
w = len(inp[0])
if any(len(row) != ... |
t4 | [{"input":[[0,0,0,0,0,0,0,2,2,2,2,2],[0,0,0,0,0,0,0,2,3,0,0,2],[0,0,0,0,0,0,0,2,0,1,0,2],[0,0,0,0,0,0,0,2,0,0,0,2],[0,0,0,0,0,0,0,2,2,2,2,2],[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,... | [[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,2,2,2,2,2],[0,0,0,0,0,0,0,2,0,0,6,2],[0,0,0,0,0,0,0,2,0,1,0,2],[0,0,0,0,0,0,0,2,3,0,0,2],[0,0,0,0,0,0,0,2,2,2,2,2]] | [[0,0,0,0,0,0,0,0,6,6,6,6],[0,0,0,0,0,0,0,0,6,0,0,6],[0,0,0,0,0,0,0,0,6,0,2,6],[0,0,0,0,0,0,0,0,6,6,6,6],[0,0,0,0,1,1,1,1,0,0,0,0],[0,0,0,0,1,0,0,1,0,0,0,0],[0,0,0,0,1,0,2,1,0,0,0,0],[0,0,0,0,1,1,1,1,0,0,0,0],[3,3,3,3,0,0,0,0,0,0,0,0],[3,0,0,3,0,0,0,0,0,0,0,0],[3,0,2,3,0,0,0,0,0,0,0,0],[3,3,3,3,0,0,0,0,0,0,0,0]] | {"train":[{"input":[[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... | """Generator for Test2/t4 scaled-rectangle task."""
from __future__ import annotations
import random
from typing import List
Grid = List[List[int]]
RED = 2
def _empty(h: int, w: int) -> Grid:
return [[0 for _ in range(w)] for _ in range(h)]
def _neighbors4(r: int, c: int) -> list[tuple[int, int]]:
retur... | """Verifier for Test2/t4 scaled-rectangle task."""
from __future__ import annotations
from typing import List
Grid = List[List[int]]
RED = 2
def _empty(h: int, w: int) -> Grid:
return [[0 for _ in range(w)] for _ in range(h)]
def _neighbors4(r: int, c: int) -> list[tuple[int, int]]:
return [(r - 1, c), (... |
t5 | "[{\"input\":[[8,8,8,0,0,0,0,0,0,0],[8,8,8,0,0,0,0,0,0,0],[8,8,8,8,0,0,0,0,0,0],[7,7,7,7,7,7,0,7,7,7(...TRUNCATED) | "[[8,8,0,0,8,8,0,0,0,0],[8,8,0,0,8,8,0,0,0,0],[8,8,0,0,0,0,0,0,0,0],[8,8,0,0,0,0,0,8,8,8],[8,8,0,0,0(...TRUNCATED) | "[[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,8,8(...TRUNCATED) | "{\"train\":[{\"input\":[[8,8,8,8,8,8,8,8,8,8,8],[8,8,8,8,8,8,8,8,8,8,8],[0,0,0,0,0,8,0,0,0,0,0],[7,(...TRUNCATED) | "\"\"\"Generator for Test2/t5 cyan-flow task.\"\"\"\n\nfrom __future__ import annotations\n\nimport (...TRUNCATED) | "\"\"\"Verifier for Test2/t5 cyan-flow task.\"\"\"\n\nfrom __future__ import annotations\n\nfrom typ(...TRUNCATED) |
t6 | "[{\"input\":[[0,0,0,0,0,0,0,0,0,0,0,0],[0,5,5,5,5,5,5,5,5,5,5,0],[0,5,0,0,0,6,0,0,0,0,5,0],[0,5,0,0(...TRUNCATED) | "[[0,0,0,0,0,0,0,0,0,0,0,0],[0,5,5,5,5,5,5,5,5,5,5,0],[0,5,0,0,5,0,0,5,0,0,5,0],[0,5,0,0,0,5,0,0,5,0(...TRUNCATED) | "[[0,0,0,0,0,0,0,0,0,0,0,0],[0,5,5,5,5,5,5,5,5,5,5,0],[0,5,2,2,5,4,4,5,3,3,5,0],[0,5,2,2,2,5,4,4,5,3(...TRUNCATED) | "{\"train\":[{\"input\":[[0,0,0,0,0,0,0,0,0,0,0,3,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,5,5,5,5,5,5,5,(...TRUNCATED) | "\"\"\"Generator for Test2/t6 partition-distance ranking task.\"\"\"\n\nfrom __future__ import annot(...TRUNCATED) | "\"\"\"Verifier for Test2/t6 partition-distance ranking task.\"\"\"\n\nfrom __future__ import annota(...TRUNCATED) |
t7 | "[{\"input\":[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,2,2,2,0],[0,0,0,0,0,2,2,2,0],[0,0,0,0,0,0,2,2,0],[0,7,7(...TRUNCATED) | "[[0,0,0,0,0,0,0,0,0,0],[0,6,6,6,0,0,0,0,9,0],[0,6,0,0,0,0,0,0,9,0],[0,6,0,0,0,0,0,9,9,0],[0,0,0,0,0(...TRUNCATED) | "[[0,0,0,0,0,0,0,0,0,0],[0,6,6,6,0,0,0,0,9,0],[0,6,0,0,0,0,0,0,9,0],[0,6,0,0,0,0,0,9,9,0],[0,0,0,0,0(...TRUNCATED) | "{\"train\":[{\"input\":[[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,8,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,(...TRUNCATED) | "\"\"\"Generator for Test2/t7 diagonal-connect-and-wrap task.\"\"\"\n\nfrom __future__ import annota(...TRUNCATED) | "\"\"\"Verifier for Test2/t7 diagonal-connect-and-wrap task.\"\"\"\n\nfrom __future__ import annotat(...TRUNCATED) |
t8 | "[{\"input\":[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[2,1,1,1,5,1,5,1,1],[1,1,1,5,1,1,5,1,2],[1,1,5(...TRUNCATED) | "[[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[1,5,2,1,1,1,1,2,5,1],[2,5,1,2,1,2,1,5,1,1],[1,5,1,1,1(...TRUNCATED) | [[0,0,0,0,0,0],[0,0,0,0,0,0],[0,0,0,0,0,0],[0,0,0,0,0,0],[0,2,0,2,0,0],[2,2,2,2,2,2]] | "{\"train\":[{\"input\":[[0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0],[1,1,1,1,5,2,2,1,1,1,5,(...TRUNCATED) | "\"\"\"Generator for Test2/t8 enclosed-body red gravity task.\"\"\"\n\nfrom __future__ import annota(...TRUNCATED) | "\"\"\"Verifier for Test2/t8 enclosed-body red gravity task.\"\"\"\n\nfrom __future__ import annotat(...TRUNCATED) |
t9 | "[{\"input\":[[0,0,1,0,0,0,0,4,0],[0,0,1,0,0,0,0,0,4],[1,1,1,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0(...TRUNCATED) | [[0,0,0,0,0,8],[0,1,1,0,0,8],[0,1,0,0,0,0],[0,1,0,0,0,0],[0,0,0,0,0,0],[2,0,0,0,5,5]] | [[1,1,8],[1,2,8],[1,5,5]] | "{\"train\":[{\"input\":[[6,6,6,6,6,6,0,0,0,0,0,0,0,0],[6,6,6,6,6,6,0,0,0,0,0,0,0,0],[0,6,6,6,0,0,0,(...TRUNCATED) | "\"\"\"Generator for Test2/t9 unique rectangle assembly task.\"\"\"\n\nfrom __future__ import annota(...TRUNCATED) | "\"\"\"Verifier for Test2/t9 unique rectangle assembly task.\"\"\"\n\nfrom __future__ import annotat(...TRUNCATED) |
t10 | "[{\"input\":[[0,1,8,6,1,0,0,0,0],[0,0,0,0,6,0,0,0,4],[0,0,0,0,0,0,0,0,4],[0,0,0,0,0,0,0,0,4],[0,0,0(...TRUNCATED) | "[[0,0,0,0,0,4,0,0,0,9],[0,0,0,0,4,0,4,0,0,0],[0,0,0,0,0,0,0,0,5,5],[2,2,2,0,0,0,0,0,0,5],[0,0,2,0,0(...TRUNCATED) | "[[6,6,0,0,0,4,0,3,3,9],[6,0,0,0,4,0,4,0,3,0],[0,0,0,0,0,0,0,0,5,5],[2,2,2,0,0,0,0,8,8,5],[0,0,2,0,0(...TRUNCATED) | "{\"train\":[{\"input\":[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[3,3,0,0,0,0,0,0,0],[3,3,0,0,0,9,9,(...TRUNCATED) | "\"\"\"Generator for Test2/t10: 180° partner fill (center-symmetric sparse overlay).\n\nRule:\n- Fo(...TRUNCATED) | "\"\"\"Verifier for Test2/t10: 180° partner fill.\"\"\"\n\nfrom __future__ import annotations\n\nfr(...TRUNCATED) |
End of preview. Expand in Data Studio
P-ARC CSV export (PotARCin Test2)
One CSV file in UTF-8. Each row is one of the fifty P-ARC tasks from PotARCin (t1.json through t50.json). Besides the usual train/test grids, each row includes the fifty-sample bundle from t<n>_samples_50.json as compact JSON (same structure as the file, without the extra whitespace from pretty-printing), plus the generator.py and verifier.py sources from the matching task folder.
Files
| File | Description |
|---|---|
p_arc_dataset.csv |
One row per task (t1–t50); column details below and in SCHEMA.json |
README.md |
Dataset card (what you are reading now) |
SCHEMA.json |
Same column layout in JSON for scripts |
Columns
| Column | Description |
|---|---|
task_id |
t1 … t50 |
train_demonstrations_json |
JSON array of training pairs {input, output}; grids are nested lists of integers |
test_input_json |
JSON grid for test[0].input |
test_output_json |
JSON grid for test[0].output |
stable_instances_50_json |
Compact JSON for the object in t<n>_samples_50.json |
generator_py |
Full generator.py source |
verifier_py |
Full verifier.py source |
Grids follow the usual ARC convention: each row is a list of cell integers.
Size and reading the CSV
The file is fairly large because big JSON blobs and full Python files sit inside cells. In Python, the standard library csv module caps field length by default; bump it before you read:
csv.field_size_limit(sys.maxsize)
Stdlib example
import csv, json, sys
csv.field_size_limit(sys.maxsize)
with open("p_arc_dataset.csv", encoding="utf-8", newline="") as f:
for row in csv.DictReader(f):
train = json.loads(row["train_demonstrations_json"])
test_in = json.loads(row["test_input_json"])
test_out = json.loads(row["test_output_json"])
samples = json.loads(row["stable_instances_50_json"])
# row["generator_py"], row["verifier_py"]
pandas
import sys, pandas as pd
import csv as _csv
_csv.field_size_limit(sys.maxsize)
df = pd.read_csv("p_arc_dataset.csv", encoding="utf-8")
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