003 • Response

puzzle_id:: 4-0d3d703e
description:: example_3
timestamp:: 24.329.071439
call_count:: 3
model:: models/gemini-exp-1114

This example follows the same pattern as the previous two, with each column containing a single color. The output grid also has three columns with each column a different single color.

input:
  width: 3
  height: 3
  colors:
    - 5: 3
    - 8: 3
    - 6: 3
  objects:
    - three columns of color
output:
  width: 3
  height: 3
  colors:
    - 1: 3
    - 9: 3
    - 2: 3
  objects:
    - three columns of color
differences:
  cells_changed: 9
  colors_changed: all colors changed to a new color
transformation:
  color value mapping 5->1, 8->9, 6->2

code_execution:

import numpy as np
input_grid = np.array([[5, 8, 6], [5, 8, 6], [5, 8, 6]])
output_grid = np.array([[1, 9, 2], [1, 9, 2], [1, 9, 2]])

def find_color_mapping(in_grid, out_grid):
    color_map = {}
    for i in range(in_grid.shape[0]):
        for j in range(in_grid.shape[1]):
            color_map[in_grid[i, j]] = out_grid[i, j]
    return color_map

color_map = find_color_mapping(input_grid, output_grid)
print(color_map)

code_execution_result: 1

{np.int64(5): np.int64(1), np.int64(8): np.int64(9), np.int64(6): np.int64(2)}

The code confirms the color mapping speculation: 5->1, 8->9, 6->2.

The natural language program for this transformation is to map the color values in the input grid to the specified color values in the output grid according to the mapping 5->1, 8->9, 6->2.

time
response time	total elapsed
19.685	66.327

usage
token type	call count	total used
Prompt	4412	9860
Response	520	1099
Total	4932	10959
Cached	0	0