003 • History
- puzzle_id:
1-3aa6fb7a
- description:
example_summary
- timestamp:
24.356.194941
- call_count:
3
Begin puzzle: 1-3aa6fb7a
example_1
input
[[0 0 0 0 0 0 0]
[0 8 0 0 0 0 0]
[0 8 8 0 0 0 0]
[0 0 0 0 8 8 0]
[0 0 0 0 0 8 0]
[0 0 0 0 0 0 0]
[0 0 0 0 0 0 0]]
output
[[0 0 0 0 0 0 0]
[0 8 1 0 0 0 0]
[0 8 8 0 0 0 0]
[0 0 0 0 8 8 0]
[0 0 0 0 1 8 0]
[0 0 0 0 0 0 0]
[0 0 0 0 0 0 0]]
images
observations
The user wants me to analyze an example input/output pair from an ARC task and describe the transformation rule in natural language.
Plan:
Describe the input grid: Note its dimensions and the arrangement of colored pixels.
Describe the output grid: Note its dimensions and the arrangement of colored pixels.
Identify differences: Pinpoint the specific pixels that changed color between the input and output.
Analyze the transformation: Formulate a rule that explains how the input was transformed into the output. Focus on the colors involved and their relative positions.
Write a natural language program: Describe the transformation rule in a step-by-step manner, ensuring it’s clear and precise enough for another agent to follow.
Input Grid Description:
The input grid is 7x7. It contains white (0) and azure (8) pixels. There are two distinct clusters of azure pixels. One is a vertical line of three pixels on the left side. The other forms an “L” shape in the bottom right corner.
Output Grid Description:
The output grid is also 7x7 and contains white (0), azure (8), and blue (1) pixels. The azure pixels retain their original positions. Two white pixels have been changed to blue.
Identify Differences:
Let’s use code to identify the specific changes.
def analyze_grid(grid):
height = len(grid)
width = len(grid[0])
colors = {}
for row in grid:
for pixel in row:
colors[pixel] = colors.get(pixel, 0) + 1
return {"height": height, "width": width, "colors": colors}
input_grid = [[0, 0, 0, 0, 0, 0, 0],
[0, 8, 0, 0, 0, 0, 0],
[0, 8, 8, 0, 0, 0, 0],
[0, 0, 0, 0, 8, 8, 0],
[0, 0, 0, 0, 0, 8, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]]
output_grid = [[0, 0, 0, 0, 0, 0, 0],
[0, 8, 1, 0, 0, 0, 0],
[0, 8, 8, 0, 0, 0, 0],
[0, 0, 0, 0, 8, 8, 0],
[0, 0, 0, 0, 1, 8, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]]
input_analysis = analyze_grid(input_grid)
output_analysis = analyze_grid(output_grid)
print("Input Analysis:", input_analysis)
print("Output Analysis:", output_analysis)
differences = []
for r in range(len(input_grid)):
for c in range(len(input_grid[0])):
if input_grid[r][c] != output_grid[r][c]:
differences.append(((r, c), input_grid[r][c], output_grid[r][c]))
print("Differences (row, col), input_color, output_color:", differences)
input:
width: 7
height: 7
colors:
0: 44
8: 5
objects:
- color: azure
pixels: 3
shape: line
position: top-left
- color: azure
pixels: 2
shape: line
position: mid-right
differences:
cells_changed: 2
colors_changed:
- white to blue
transformation:
- The white cell to the right of the top-most azure pixel changed to blue.
- The white cell to the left of the bottom-most azure pixel in the right cluster changed to blue.
Natural Language Program:
Context: The input is a 7x7 grid containing white and azure pixels. There are two distinct objects formed by contiguous azure pixels: a vertical line on the left and an “L” shape on the right.
Initialization: Create a copy of the input grid to serve as the initial state of the output grid.
Color Palette: The output grid will contain white, azure, and blue pixels.
Transformation:
Identify the topmost azure pixel in the vertical line of azure pixels.
Change the color of the white pixel immediately to the right of this azure pixel to blue.
Identify the bottommost azure pixel in the “L” shaped cluster of azure pixels on the right.
Change the color of the white pixel immediately to the left of this azure pixel to blue.
The resulting grid is the final output.
example_2
input
[[0 0 0 0 8 8 0]
[0 0 0 0 0 8 0]
[0 0 8 0 0 0 0]
[0 0 8 8 0 0 0]
[0 0 0 0 0 0 0]
[0 0 0 0 8 0 0]
[0 0 0 8 8 0 0]]
output
[[0 0 0 0 8 8 0]
[0 0 0 0 1 8 0]
[0 0 8 1 0 0 0]
[0 0 8 8 0 0 0]
[0 0 0 0 0 0 0]
[0 0 0 1 8 0 0]
[0 0 0 8 8 0 0]]
images
observations
The user wants me to analyze the second example of the puzzle and describe the transformation rule.
Plan:
Describe the input grid: Note its dimensions and the arrangement of colored pixels, paying attention to objects.
Describe the output grid: Note its dimensions and the arrangement of colored pixels.
Identify differences: Pinpoint the specific pixels that changed color between the input and output.
Analyze the transformation: Formulate a rule that explains how the input was transformed into the output, considering the previous example.
Write a natural language program: Describe the transformation rule in a step-by-step manner, ensuring consistency with the first example.
Input Grid Description:
The input grid is 7x7. It contains white (0) and azure (8) pixels. There are two distinct clusters of azure pixels. One forms a backward “L” shape in the top right corner. The other forms a vertical line of two pixels and a separate single pixel below it on the left side.
Output Grid Description:
The output grid is also 7x7 and contains white (0), azure (8), and blue (1) pixels. The azure pixels retain their original positions. Three white pixels have been changed to blue.
Identify Differences:
Let’s use code to identify the specific changes.
def analyze_grid(grid):
height = len(grid)
width = len(grid[0])
colors = {}
for row in grid:
for pixel in row:
colors[pixel] = colors.get(pixel, 0) + 1
return {"height": height, "width": width, "colors": colors}
input_grid = [[0, 0, 0, 0, 8, 8, 0],
[0, 0, 0, 0, 0, 8, 0],
[0, 0, 8, 0, 0, 0, 0],
[0, 0, 8, 8, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 8, 0, 0],
[0, 0, 0, 8, 8, 0, 0]]
output_grid = [[0, 0, 0, 0, 8, 8, 0],
[0, 0, 0, 0, 1, 8, 0],
[0, 0, 8, 1, 0, 0, 0],
[0, 0, 8, 8, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 8, 0, 0],
[0, 0, 0, 8, 8, 0, 0]]
input_analysis = analyze_grid(input_grid)
output_analysis = analyze_grid(output_grid)
print("Input Analysis:", input_analysis)
print("Output Analysis:", output_analysis)
differences = []
for r in range(len(input_grid)):
for c in range(len(input_grid[0])):
if input_grid[r][c] != output_grid[r][c]:
differences.append(((r, c), input_grid[r][c], output_grid[r][c]))
print("Differences (row, col), input_color, output_color:", differences)
input:
width: 7
height: 7
colors:
0: 44
8: 5
objects:
- color: azure
pixels: 3
shape: L
position: top-right
- color: azure
pixels: 2
shape: line
position: mid-left
- color: azure
pixels: 1
shape: single
position: bottom-left
differences:
cells_changed: 3
colors_changed:
- white to blue
transformation:
- The white cell to the left of the bottom-most azure pixel in the top-right cluster changed to blue.
- The white cell to the right of the top-most azure pixel in the mid-left cluster changed to blue.
- The white cell to the right of the single azure pixel in the bottom-left changed to blue.
Natural Language Program:
Context: The input is a 7x7 grid containing white and azure pixels. There are three distinct objects formed by contiguous azure pixels.
Initialization: Create a copy of the input grid to serve as the initial state of the output grid.
Color Palette: The output grid will contain white, azure, and blue pixels.
Transformation:
Identify the bottommost azure pixel in the backward “L” shaped cluster of azure pixels in the top right. Change the color of the white pixel immediately to its left to blue.
Identify the topmost azure pixel in the vertical line of azure pixels on the left. Change the color of the white pixel immediately to its right to blue.
Identify the single azure pixel in the bottom left. Change the color of the white pixel immediately to its right to blue.
examples summary
INSTRUCTIONS:
This is your chance to review what you have learned from the examples
summarize your observations to explain the transformation of the input to output
use code_execution to re-investigate properties, patterns and differences in the grids to confirm your predictions
generate your final step by step natural language program
See also