SyncValsverifier → artifact → classifier → verdict
SyncVals · Trajectory

game-of-life-step

claude-code claude-haiku-4-5 ✓ resolved GOOD_SUCCESS 11 turns · 45 tok in · 2,564 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 tests pass: test_output_exists, test_shipped_dimensions_and_values, test_shipped_walls_preserved, test_shipped_correct (verified against reference `_evolve()` implementation), test_not_identity_copy, and test_hidden_alternates (12 procedurally-generated test cases). Agent's implementation correctly uses toroidal wrap-around with `(r + dr) % rows` and `(c + dc) % cols`, only counts living neighbors (value 1) excluding walls (value 2), and applies Conway survival {2,3} / birth {3} rules identically to the reference implementation."
Root causeThe agent thoroughly understood the detailed instruction, correctly implemented toroidal Game of Life with walls, and all comprehensive tests (including 12 hidden procedurally-generated cases) pass legitimately.
RecommendationN/A - task is fine
Trajectory
Tool-by-tool agent trajectory
10 tool calls · 3 tool types · 20 steps
I'll read the current state of the project and then implement the Game of Life evolution with the specified rules.

<tmp>/workspace

contents
EISDIR: illegal operation on a directory, read '<tmp>/workspace'
Let me check what files are available in the 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 input files and the stub:

<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 Game of Life evolution. The key points are: 1. Toroidal wrap-around (edges connect) 2. Walls (value 2) stay fixed and don't count as neighbors 3. Standard Conway rules: cells with 2-3 neighbors survive, cells with exactly 3 neighbors born 4. Advance the board by the requested number of generations

<tmp>/workspace/main.py

− removed
# 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
def count_neighbors(grid, row, col):
    """Count living neighbors in a toroidal grid, ignoring walls."""
    rows = len(grid)
    cols = len(grid[0])
    count = 0

    # Check all 8 neighbors with wrap-around
    for dr in [-1, 0, 1]:
        for dc in [-1, 0, 1]:
            if dr == 0 and dc == 0:
                continue
            nr = (row + dr) % rows
            nc = (col + dc) % cols
            if grid[nr][nc] == 1:
                count += 1

    return count


def evolve_generation(grid):
    """Evolve the grid one generation forward."""
    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):
            if grid[r][c] == 2:
                # Walls stay walls
                new_grid[r][c] = 2
            else:
                neighbors = count_neighbors(grid, r, c)
                if grid[r][c] == 1:
                    # Living cell survives with 2-3 neighbors
                    new_grid[r][c] = 1 if neighbors in (2, 3) else 0
                else:
                    # Empty cell becomes alive with exactly 3 neighbors
                    new_grid[r][c] = 1 if neighbors == 3 else 0

    return new_grid


# Evolve the grid for the requested number of generations
next_grid = grid
for _ in range(generations):
    next_grid = evolve_generation(next_grid)
Now let me test the implementation to make sure it produces the correct output:
shell
python main.py
output
Exit code 127
(eval):1: command not found: python
Let me try python3:
shell
python3 main.py
output
(Bash completed with no output)
Good! Let me verify the output was created 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 output file has been created with the correct format (no header, comma-separated integers). Let me verify the logic is working by tracing through one generation manually to ensure the wrap-around and wall handling are correct:
shell
cat > /tmp/verify.py << 'EOF'
# Verification: trace through the evolution manually
grid = [
    [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],
]

def count_neighbors(grid, row, col):
    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
            nr = (row + dr) % rows
            nc = (col + dc) % cols
            if grid[nr][nc] == 1:
                count += 1
    return count

# Check a few cells to verify
print(f"Cell (0,0) value={grid[0][0]}, neighbors={count_neighbors(grid, 0, 0)}")
print(f"Cell (0,1) value={grid[0][1]}, neighbors={count_neighbors(grid, 0, 1)}")
print(f"Cell (2,1) value={grid[2][1]}, neighbors={count_neighbors(grid, 2, 1)}")  # Wall cell
print(f"Cell (1,4) value={grid[1][4]}, neighbors={count_neighbors(grid, 1, 4)}")
EOF
python3 /tmp/verify.py
output
Cell (0,0) value=1, neighbors=5
Cell (0,1) value=1, neighbors=3
Cell (2,1) value=2, neighbors=0
Cell (1,4) value=1, neighbors=2
Excellent. The neighbor counting is working correctly, the wall at (2,1) correctly returns 0 neighbors (it's not counted, and it has no living neighbors to count). The implementation is complete and correct. **Summary:** I've implemented the Game of Life evolution for a toroidal grid with walls. The code reads the starting board and configuration, applies the Conway rules for the requested number of generations (3 in this case), and outputs the final board to `output/next.csv`. Key features: - Wrap-around topology (top/bottom and left/right edges connect) - Walls (value 2) remain fixed and don't count toward neighbor tallies - Standard birth (exactly 3 neighbors) and survival (2-3 neighbors) rules - All cells update simultaneously each generation
Excellent. The neighbor counting is working correctly, the wall at (2,1) correctly returns 0 neighbors (it's not counted, and it has no living neighbors to count). The implementation is complete and correct. **Summary:** I've implemented the Game of Life evolution for a toroidal grid with walls. The code reads the starting board and configuration, applies the Conway rules for the requested number of generations (3 in this case), and outputs the final board to `output/next.csv`. Key features: - Wrap-around topology (top/bottom and left/right edges connect) - Walls (value 2) remain fixed and don't count toward neighbor tallies - Standard birth (exactly 3 neighbors) and survival (2-3 neighbors) rules - All cells update simultaneously each generation
Final diff , what the agent changed
--- a/main.py
+++ b/main.py
@@ -19,9 +19,52 @@
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_neighbors(grid, row, col):
+ """Count living neighbors in a toroidal grid, ignoring walls."""
+ rows = len(grid)
+ cols = len(grid[0])
+ count = 0
+
+ # Check all 8 neighbors with wrap-around
+ for dr in [-1, 0, 1]:
+ for dc in [-1, 0, 1]:
+ if dr == 0 and dc == 0:
+ continue
+ nr = (row + dr) % rows
+ nc = (col + dc) % cols
+ if grid[nr][nc] == 1:
+ count += 1
+
+ return count
+
+
+def evolve_generation(grid):
+ """Evolve the grid one generation forward."""
+ 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):
+ if grid[r][c] == 2:
+ # Walls stay walls
+ new_grid[r][c] = 2
+ else:
+ neighbors = count_neighbors(grid, r, c)
+ if grid[r][c] == 1:
+ # Living cell survives with 2-3 neighbors
+ new_grid[r][c] = 1 if neighbors in (2, 3) else 0
+ else:
+ # Empty cell becomes alive with exactly 3 neighbors
+ new_grid[r][c] = 1 if neighbors == 3 else 0
+
+ return new_grid
+
+
+# Evolve the grid for the requested number of generations
next_grid = grid
+for _ in range(generations):
+ next_grid = evolve_generation(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 0.28s ===============================

Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_e638457c5a0d400b. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.

Trial trial_e638457c5a0d400b · verifier authoritative; classifier explanatory.