SyncVals · Trajectory

game-of-life-step

claude-code claude-haiku-4-5 ✓ resolved GOOD_SUCCESS 10 turns · 42 tok in · 2,254 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

EvidenceAgent implemented toroidal Game of Life with walls correctly. All 6 tests passed: test_output_exists, test_shipped_dimensions_and_values, test_shipped_walls_preserved, test_shipped_correct, test_not_identity_copy, and test_hidden_alternates (which tests 12 hidden board configurations with randomized seeds 42, 101, 202, ..., 4242). Agent's code matches reference solution structure with correct neighbor counting (using modulo wrapping, excluding walls), correct Conway rules (survive 2-3 neighbors, birth on 3), wall preservation, and multi-generation evolution loop.

Root causeAgent correctly understood a complex specification with multiple constraint classes (special cell values, toroidal topology, specific update rules) and implemented the solution properly. The implementation passed all verification tests including 12 hidden test cases with varied board sizes and wall configurations.

RecommendationN/A - task is fine

Trajectory

Tool-by-tool agent trajectory

9 tool calls · 3 tool types · 18 steps

I'll implement the Conway's Game of Life with wrap-around topology and special wall handling. Let me start by reading the existing code and data files.

<tmp>/workspace

contents

EISDIR: illegal operation on a directory, read '<tmp>/workspace'

Let me check what files are in the workspace.

List files in workspace

shell

find <tmp>/workspace -type f | head -20

output

<tmp>/workspace/instruction.md
<tmp>/workspace/task.toml
<tmp>/workspace/main.py
<tmp>/workspace/environment/README.md
<tmp>/workspace/data/config.json
<tmp>/workspace/data/grid.csv

Now let me read the main files:

<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/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

<tmp>/workspace/data/config.json

contents

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

Perfect! Now I'll implement the toroidal Game of Life evolution. The key points: 1. Walls (value 2) stay as walls and don't count as living neighbors 2. Wrap-around on all edges (toroidal) 3. Standard Conway rules: living cell survives with 2-3 neighbors, empty cell births with exactly 3 neighbors 4. Apply the rules for the specified number of generations

<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_living_neighbors(grid, row, col):
    """Count living neighbors on a toroidal grid, excluding walls."""
    rows = len(grid)
    cols = len(grid[0])
    count = 0

    for dr in [-1, 0, 1]:
        for dc in [-1, 0, 1]:
            if dr == 0 and dc == 0:
                continue
            neighbor_row = (row + dr) % rows
            neighbor_col = (col + dc) % cols
            cell = grid[neighbor_row][neighbor_col]
            if cell == 1:
                count += 1

    return count

def evolve_board(grid):
    """Apply one generation step under Conway's Game of Life rules."""
    rows = len(grid)
    cols = len(grid[0])
    new_grid = [[0] * cols for _ in range(rows)]

    for r in range(rows):
        for c in range(cols):
            cell = grid[r][c]

            if cell == 2:
                new_grid[r][c] = 2
            else:
                neighbors = count_living_neighbors(grid, r, c)
                if cell == 1:
                    new_grid[r][c] = 1 if neighbors in [2, 3] else 0
                else:
                    new_grid[r][c] = 1 if neighbors == 3 else 0

    return new_grid

# Evolve the board for the requested number of generations
next_grid = grid
for _ in range(generations):
    next_grid = evolve_board(next_grid)

Now let me test the implementation by running it:

Run the Game of Life implementation

shell

python main.py

output

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

Let me try python3:

Run the Game of Life implementation

shell

python3 main.py

output

(Bash completed with no output)

Good! Let me verify the output was created and check the results:

<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 implementation has successfully evolved the board 3 generations. Let me verify the logic is correct by manually checking one step: **Initial board (gen 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 ``` The output shows the board after 3 generations has been correctly calculated with: - Walls (value 2) preserved in their original positions at [2,1] and [3,6] - Living cells evolving according to Conway's rules - Toroidal wrapping applied (edges wrap around) The implementation correctly: 1. ✅ Reads the board and configuration 2. ✅ Counts living neighbors while ignoring walls (treating them as neither alive nor neighbors) 3. ✅ Applies Conway's survival rule (2-3 neighbors) and birth rule (exactly 3 neighbors) 4. ✅ Handles toroidal topology with modulo wrapping on all edges 5. ✅ Preserves walls as permanent value 2 6. ✅ Updates all cells simultaneously from the previous generation 7. ✅ Evolves for the exact number of generations requested (3) 8. ✅ Outputs to `output/next.csv` in the correct format The task is complete!

Final diff , what the agent changed

--- a/main.py

+++ b/main.py

@@ -19,9 +19,49 @@

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_living_neighbors(grid, row, col):

+ """Count living neighbors on a toroidal grid, excluding walls."""

+ rows = len(grid)

+ cols = len(grid[0])

+ count = 0

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

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

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

+ continue

+ neighbor_row = (row + dr) % rows

+ neighbor_col = (col + dc) % cols

+ cell = grid[neighbor_row][neighbor_col]

+ if cell == 1:

+ count += 1

+ return count

+def evolve_board(grid):

+ """Apply one generation step under Conway's Game of Life rules."""

+ rows = len(grid)

+ cols = len(grid[0])

+ new_grid = [[0] * cols for _ in range(rows)]

+ for r in range(rows):

+ for c in range(cols):

+ cell = grid[r][c]

+ if cell == 2:

+ new_grid[r][c] = 2

+ else:

+ neighbors = count_living_neighbors(grid, r, c)

+ if cell == 1:

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

+ else:

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

+ return new_grid

+# Evolve the board for the requested number of generations

next_grid = grid

+for _ in range(generations):

+ next_grid = evolve_board(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_1fcbbc9023394bde. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.

Trial trial_1fcbbc9023394bde · verifier authoritative; classifier explanatory.