005 • Response

puzzle_id:: 4-0d3d703e
description:: example_summary
timestamp:: 24.329.071439
call_count:: 5
model:: models/gemini-exp-1114

Ruminate Phase

All four examples present the same pattern. The input grids consist of three columns, each of a single color. The output grids are the same size as the input grids, also with three columns, and each column is a single color. The transformation in each example is the same: each color in the input grid is mapped to a specific color in the output grid. Each example provides a unique color mapping.

The transformation rule in all examples is a direct color mapping. The input colors are replaced with corresponding output colors according to a consistent mapping table, specific to each example.

Natural language program

For the test grid, identify the color values in each of the three columns of the input grid. Create an output grid of the same size as the input grid. Apply the color mapping based on the learned mappings in the examples: for each cell in the input grid, replace its color with the corresponding color defined in the mapping table for that example. The resulting output grid will have three columns, each filled with a single, mapped color.

To confirm the natural language program I will check for consistent mappings across all examples, then run the program in code_execution on the test input after it’s revealed.

Summary of color mappings

Example 1: 3->4, 1->5, 2->6
Example 2: 2->6, 3->4, 8->9
Example 3: 5->1, 8->9, 6->2
Example 4: 9->8, 4->3, 2->6

I note that there is some overlap in mappings. This further supports the consistency of the transformation program - colors map to the same output across examples:

2 maps to 6 in examples 1, 2, and 4
3 maps to 4 in examples 1 and 2
8 maps to 9 in examples 2 and 3

This suggests a global mapping rule, though the provided examples do not fully define this rule. For the test puzzle, we should apply the specific mapping based on the test input’s color values, following the pattern observed in the examples.

code_execution:

import numpy as np

# Example mappings
example_mappings = [
    {3: 4, 1: 5, 2: 6},
    {2: 6, 3: 4, 8: 9},
    {5: 1, 8: 9, 6: 2},
    {9: 8, 4: 3, 2: 6}
]

# Check for consistent mappings
consistent_mappings = {}
for mapping in example_mappings:
    for in_val, out_val in mapping.items():
        if in_val in consistent_mappings:
            if consistent_mappings[in_val] != out_val:
                print(f"Inconsistency found for {in_val}: {consistent_mappings[in_val]} vs {out_val}")
        else:
            consistent_mappings[in_val] = out_val

print("Consistent mappings found (if no inconsistencies were reported above):")
print(consistent_mappings)

code_execution_result: 1

Consistent mappings found (if no inconsistencies were reported above):
{3: 4, 1: 5, 2: 6, 8: 9, 5: 1, 6: 2, 9: 8, 4: 3}

This confirms the mapping is consistent.

time
response time	total elapsed
27.794	112.916

usage
token type	call count	total used
Prompt	5794	21182
Response	787	2406
Total	6581	23588
Cached	0	0