grid_turing_robot.json

{
  "task_id": "grid_turing_robot",
  "name": "Grid Turing Robot",
  "category": "Combinatorial Optimization",
  "base_image": "python310",
  "platform": "linux/amd64",
  "internet": false,
  "cwd": "/home/workspace/grid_turing_robot",
  "submit_paths": [
    "solution.py"
  ],
  "work": {
    "image_tag": "ac7ea62005f7",
    "specs_dir": "/home/workspace/grid_turing_robot",
    "agent_query": "## Grid Turing Robot - Transition Rule Design (AHC056)\n\nWrite `solution.py` in the project root that reads from stdin and writes to stdout.\n\n---\n\n## Problem Overview\n\nRead `README.md` and `tools/README.md` for full problem details. A baseline `solution.py` already exists (it produces syntactically valid but low-quality output). Your job is to improve it.\n\n---\n\n## Evaluation\n\nYour solution is scored on **50 fixed test cases**. Final score = sum of individual case scores. **Lower is better.**\n\n---\n\n## Local Testing\n\nGenerate local random tests with `./tools/bin/gen <seed>`, using seeds in the range **0..10000** only.\n\n```bash\n# Generate a random test case (seed-based, deterministic)\n./tools/bin/gen 0 > input.txt\n\n# Run your solution\npython3 solution.py < input.txt > output.txt\n\n# Score output (Lower is better)\n./tools/bin/tester input.txt output.txt\n# Outputs to stderr: Score = <N>\n```\n\n---\n\n## Rules\n\n- Write your solution as `solution.py` in the project root directory\n- Do NOT modify files in `tools/`\n- Use `tools/bin/gen` and `tools/bin/tester` for local testing\n- For local scoring, use only `./tools/bin/tester`; do not use `tools/src/verifier.py` for scores\n- Your program should read from stdin and write to stdout\n- Run your solution to completion and verify with the tester before finishing\n"
  },
  "judge": {
    "image_tag": "3c6f50ac4fe0",
    "eval_cmd": "cd /home/workspace/grid_turing_robot && python3 /tmp/eval_grid_turing_robot.py",
    "eval_timeout": 600,
    "parser": "score_sum",
    "score_direction": "minimize",
    "selection": "score_first",
    "rescale": {
      "kind": "piecewise_min",
      "baseline": 9025986.0,
      "rank30": 4490.0,
      "rank1": 1575.0,
      "super_anchor": 554.75
    }
  }
}