SyncVals · Trajectory

game-of-life-step

claude-code claude-haiku-4-5 ✓ resolved GOOD_SUCCESS 8 turns · 35 tok in · 2,165 tok out ↑ View task

Solved from the instruction alone, tests/ and solution/ were withheld from the agent's workspace and restored only for grading.

Reward = tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.

Classification , post-hoc; cannot change the reward

GOOD_SUCCESSLegitimate solve, implements the asked-for behavior; tests verify real functionality.

SubtypeLegitimate Solution

EvidenceAll 6 pytest tests passed including `test_hidden_alternates` which runs 12 procedurally generated test cases with random board layouts, wall configurations, and generation counts. Agent's implementation correctly: (1) wraps neighbors using modulo arithmetic for toroidal topology, (2) counts only live cells (1s) and never counts walls (2s), (3) applies Conway rules (live cells survive with 2-3 neighbors, empty cells born with 3), (4) preserves walls as value 2, (5) updates all cells simultaneously. Agent's `count_live_neighbors()` and `evolve_once()` functions match the reference solution's logic exactly: `if grid[nr][nc] == 1: count += 1` (not checking for 2s) and `new_grid[r][c] = 1 if live_neighbors in [2, 3] else 0` (correct survival rule).

Root causeThe agent successfully solved a complex algorithmic task by carefully reading the detailed instruction.md, understanding the toroidal wrap-around requirement, implementing correct neighbor-counting logic that excludes walls, and applying Conway's rules properly across multiple generations. The implementation was verified against both the shipped example and 12 hidden procedurally-generated test cases with varying board sizes, wall densities, and generation counts.

RecommendationN/A - task is fine

Trajectory

Tool-by-tool agent trajectory

7 tool calls · 3 tool types · 14 steps

I'll read the current stub and the input files to understand the structure, then implement the Game-of-Life evolution with toroidal wrap-around.

<tmp>/workspace/main.py

contents

1	# Starter stub for the toroidal Game-of-Life task.
2	#
3	# It reads the starting board from data/grid.csv and the run settings from
4	# data/config.json, then (for now) just copies the board straight through
5	# to output/next.csv WITHOUT advancing any generations. Replace the body so
6	# it actually evolves the board the requested number of steps under the rules
7	# described in the task.
8	
9	import csv
10	import json
11	import os
12	
13	with open("data/grid.csv") as f:
14	    grid = [[int(x) for x in row] for row in csv.reader(f) if row]
15	
16	with open("data/config.json") as f:
17	    config = json.load(f)
18	
19	generations = int(config["generations"])  # number of steps to advance
20	topology = config["topology"]
21	
22	# TODO: replace this passthrough with the evolved board. As written it ignores
23	# the rules and the generation count, so it does not produce the requested board.
24	next_grid = grid
25	
26	os.makedirs("output", exist_ok=True)
27	with open("output/next.csv", "w", newline="") as f:
28	    csv.writer(f).writerows(next_grid)
29

<tmp>/workspace/data/config.json

contents

1	{"topology": "toroidal", "generations": 3}
2

<tmp>/workspace/data/grid.csv

contents

1	1,1,0,0,0,0,0,1
2	0,0,0,0,1,1,0,1
3	0,2,0,0,1,0,0,0
4	0,0,0,0,0,0,2,0
5	1,0,0,0,0,0,0,0
6	1,0,1,1,0,0,0,1
7

Now I'll implement the toroidal Game-of-Life evolution. The key points are: - Count live neighbors (only 1s, not 2s) in all 8 directions with wrapping - Apply Conway rules: live cells with 2-3 neighbors survive, empty cells with exactly 3 neighbors are born - Walls (2s) never change - Process all cells simultaneously before updating

<tmp>/workspace/main.py

− removed

generations = int(config["generations"])  # number of steps to advance
topology = config["topology"]

# TODO: replace this passthrough with the evolved board. As written it ignores
# the rules and the generation count, so it does not produce the requested board.
next_grid = grid

+ added

generations = int(config["generations"])  # number of steps to advance
topology = config["topology"]

def count_live_neighbors(grid, row, col):
    """Count live neighbors (1s only) in toroidal topology, skipping walls."""
    height = len(grid)
    width = len(grid[0])
    count = 0
    for dr in [-1, 0, 1]:
        for dc in [-1, 0, 1]:
            if dr == 0 and dc == 0:
                continue
            nr = (row + dr) % height
            nc = (col + dc) % width
            if grid[nr][nc] == 1:
                count += 1
    return count

def evolve_once(grid):
    """Apply one generation of Game-of-Life rules."""
    height = len(grid)
    width = len(grid[0])
    new_grid = [[0] * width for _ in range(height)]

    for r in range(height):
        for c in range(width):
            if grid[r][c] == 2:
                new_grid[r][c] = 2
            else:
                live_neighbors = count_live_neighbors(grid, r, c)
                if grid[r][c] == 1:
                    new_grid[r][c] = 1 if live_neighbors in [2, 3] else 0
                else:
                    new_grid[r][c] = 1 if live_neighbors == 3 else 0

    return new_grid

next_grid = grid
for _ in range(generations):
    next_grid = evolve_once(next_grid)

Now let me run the script to verify it works correctly.

Run the Game-of-Life evolution script

shell

python main.py

output

Exit code 127
(eval):1: command not found: python

Let me try with python3:

Run the Game-of-Life evolution script with python3

shell

python3 main.py

output

(Bash completed with no output)

Good! The script ran successfully. Let me verify the output was generated and check a few cells to make sure the logic is working correctly.

<tmp>/workspace/output/next.csv

contents

1	0,1,1,1,0,1,0,0
2	0,1,1,0,0,0,0,0
3	0,2,0,0,0,0,0,0
4	1,1,0,0,0,0,2,0
5	1,1,0,0,0,0,0,0
6	1,0,1,0,0,0,0,0
7

Perfect! The script has successfully evolved the board through 3 generations. Let me verify the logic by tracing through one example cell to confirm the implementation is correct. Starting board (generation 0): ``` 1,1,0,0,0,0,0,1 0,0,0,0,1,1,0,1 0,2,0,0,1,0,0,0 0,0,0,0,0,0,2,0 1,0,0,0,0,0,0,0 1,0,1,1,0,0,0,1 ``` Let me trace cell (0,1) in the starting board (value 1): - Its 8 neighbors in toroidal space (wrapping around edges) would be checked - If it has 2 or 3 live neighbors, it survives - The final output shows cell (0,1) is 1, which indicates it survived at least through the evolution The output has been written to `output/next.csv` in the correct format with: - Same dimensions as input (6 rows × 8 columns) - Comma-separated integers - Walls (2s) preserved at their original positions - Live/dead cells (1s/0s) updated according to the Game-of-Life rules The implementation correctly: 1. **Wraps around** using modulo arithmetic for all 8 neighbor directions 2. **Skips walls** when counting live neighbors (only counts 1s, not 2s) 3. **Preserves walls** in the output (they always remain 2) 4. **Applies Conway rules**: live cells survive with 2-3 neighbors, empty cells are born with exactly 3 5. **Updates synchronously**: all cells are decided based on the current generation, not modified on-the-fly Done! The board has been evolved 3 generations forward and saved to `output/next.csv`.

Final diff , what the agent changed

--- a/main.py

+++ b/main.py

@@ -19,9 +19,43 @@

generations = int(config["generations"]) # number of steps to advance

topology = config["topology"]

-# TODO: replace this passthrough with the evolved board. As written it ignores

-# the rules and the generation count, so it does not produce the requested board.

+def count_live_neighbors(grid, row, col):

+ """Count live neighbors (1s only) in toroidal topology, skipping walls."""

+ height = len(grid)

+ width = len(grid[0])

+ count = 0

+ for dr in [-1, 0, 1]:

+ for dc in [-1, 0, 1]:

+ if dr == 0 and dc == 0:

+ continue

+ nr = (row + dr) % height

+ nc = (col + dc) % width

+ if grid[nr][nc] == 1:

+ count += 1

+ return count

+def evolve_once(grid):

+ """Apply one generation of Game-of-Life rules."""

+ height = len(grid)

+ width = len(grid[0])

+ new_grid = [[0] * width for _ in range(height)]

+ for r in range(height):

+ for c in range(width):

+ if grid[r][c] == 2:

+ new_grid[r][c] = 2

+ else:

+ live_neighbors = count_live_neighbors(grid, r, c)

+ if grid[r][c] == 1:

+ new_grid[r][c] = 1 if live_neighbors in [2, 3] else 0

+ else:

+ new_grid[r][c] = 1 if live_neighbors == 3 else 0

+ return new_grid

next_grid = grid

+for _ in range(generations):

+ next_grid = evolve_once(next_grid)

os.makedirs("output", exist_ok=True)

with open("output/next.csv", "w", newline="") as f:

--- /dev/null

+++ b/output/next.csv

@@ -0,0 +1,6 @@

+0,1,1,1,0,1,0,0

+0,1,1,0,0,0,0,0

+0,2,0,0,0,0,0,0

+1,1,0,0,0,0,2,0

+1,1,0,0,0,0,0,0

+1,0,1,0,0,0,0,0

Verifier output , the proof behind the reward

tests/test.sh

============================= test session starts ==============================
platform darwin -- Python 3.12.4, pytest-8.4.1, pluggy-1.6.0 -- /Library/Frameworks/Python.framework/Versions/3.12/bin/python3
cachedir: .pytest_cache
rootdir: <tmp>/workspace
plugins: anyio-4.12.1, respx-0.22.0, mock-3.15.1, asyncio-0.21.1
asyncio: mode=Mode.STRICT
collecting ... collected 6 items

tests/test_outputs.py::test_output_exists PASSED                         [ 16%]
tests/test_outputs.py::test_shipped_dimensions_and_values PASSED         [ 33%]
tests/test_outputs.py::test_shipped_walls_preserved PASSED               [ 50%]
tests/test_outputs.py::test_shipped_correct PASSED                       [ 66%]
tests/test_outputs.py::test_not_identity_copy PASSED                     [ 83%]
tests/test_outputs.py::test_hidden_alternates PASSED                     [100%]

============================== 6 passed in 0.26s ===============================

Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_438644008d474ab4. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.

Trial trial_438644008d474ab4 · verifier authoritative; classifier explanatory.