002 • Response

puzzle_id:: 4-0d3d703e
description:: example_2
timestamp:: 24.329.071439
call_count:: 2
model:: models/gemini-exp-1114

This example is very similar to example 1, with each column containing a different color. The output grid has the same dimensions as the input and also shows columns of a single color.

input:
  width: 3
  height: 3
  colors:
    - 2: 3
    - 3: 3
    - 8: 3
  objects:
    - three columns of color
output:
  width: 3
  height: 3
  colors:
    - 6: 3
    - 4: 3
    - 9: 3
  objects:
    - three columns of color
differences:
  cells_changed: 9
  colors_changed: all colors changed to a new color
transformation:
  color value mapping 2->6, 3->4, 8->9

code_execution:

import numpy as np
input_grid = np.array([[2, 3, 8], [2, 3, 8], [2, 3, 8]])
output_grid = np.array([[6, 4, 9], [6, 4, 9], [6, 4, 9]])

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(2): np.int64(6), np.int64(3): np.int64(4), np.int64(8): np.int64(9)}

The code confirms the color mapping speculation: 2->6, 3->4, 8->9.

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 2->6, 3->4, 8->9.

time
response time	total elapsed
18.266	46.561

usage
token type	call count	total used
Prompt	3292	5448
Response	524	579
Total	3816	6027
Cached	0	0