002 • Response

puzzle_id:: 4-0d3d703e
description:: example_2
timestamp:: 24.356.195518
call_count:: 2
model:: gemini-2.0-flash-thinking-exp-1219

The user wants me to analyze the second example of the ARC task. I need to follow the same steps as before: describe the input and output grids, use code to analyze them, and formulate a natural language program for the transformation.

Plan:

Describe the input grid.
Describe the output grid.
Use code to analyze the input and output grids.
Formulate a natural language program for the transformation.

Okay, let’s analyze the second example.

The input grid is a 3x3 grid. The first column is red, the second column is green, and the third column is azure.

The output grid is also a 3x3 grid. The first column is magenta, the second column is yellow, and the third column is maroon.

input:
  width: 3
  height: 3
  colors:
    2: 3 (red)
    3: 3 (green)
    8: 3 (azure)
  objects:
    - size: 3, position: column 1, color: red
    - size: 3, position: column 2, color: green
    - size: 3, position: column 3, color: azure

output:
  width: 3
  height: 3
  colors:
    6: 3 (magenta)
    4: 3 (yellow)
    9: 3 (maroon)
  objects:
    - size: 3, position: column 1, color: magenta
    - size: 3, position: column 2, color: yellow
    - size: 3, position: column 3, color: maroon

Now let’s use code to identify the differences.

def analyze_grid(grid):
    height = len(grid)
    width = len(grid[0])
    colors = {}
    for row in grid:
        for cell in row:
            colors[cell] = colors.get(cell, 0) + 1
    return {"width": width, "height": height, "colors": colors}

input_grid = [[2, 3, 8], [2, 3, 8], [2, 3, 8]]
output_grid = [[6, 4, 9], [6, 4, 9], [6, 4, 9]]

input_analysis = analyze_grid(input_grid)
output_analysis = analyze_grid(output_grid)

print("Input analysis:", input_analysis)
print("Output analysis:", output_analysis)

differences = {}
if input_analysis['width'] != output_analysis['width'] or input_analysis['height'] != output_analysis['height']:
    differences['size_change'] = 'yes'
else:
    differences['size_change'] = 'no'

changed_cells = 0
color_changes = {}
for r in range(input_analysis['height']):
    for c in range(input_analysis['width']):
        if input_grid[r][c] != output_grid[r][c]:
            changed_cells += 1
            old_color = input_grid[r][c]
            new_color = output_grid[r][c]
            if old_color not in color_changes:
                color_changes[old_color] = new_color

differences['cells_changed'] = changed_cells
differences['color_changes'] = color_changes

print("Differences:", differences)

differences:
  cells_changed: 9
  colors_changed: {2: 6, 3: 4, 8: 9}
  transformation:
    - It appears each color in the input maps consistently to a new color in the output.
    - Red (2) maps to Magenta (6).
    - Green (3) maps to Yellow (4).
    - Azure (8) maps to Maroon (9).
    - The grid dimensions remain the same.

Natural language program:

The input grid is a 3x3 grid with three distinct colors arranged in columns: red in the first, green in the second, and azure in the third. The output grid has the same dimensions. To generate the output grid, iterate through each pixel in the input grid. If the pixel is red, change its color to magenta. If the pixel is green, change its color to yellow. If the pixel is azure, change its color to maroon. The positions of the colors remain the same; only the colors themselves are changed according to this mapping.

time
response time	total elapsed
8.525	17.532

usage
token type	call count	total used
Prompt	4218	6804
Response	1038	2078
Total	5256	8882
Cached	0	0