{
  "hub_commit": "ede75c00118e9808e5099da43c26f4619cc3bc37",
  "case": {
    "id": "pendulum-lqr-state-cost",
    "title": "Use positive state costs for this bounded LQR example",
    "resolution": "This adapter requires positive state costs and checks the discrete linear closed-loop eigenvalues. This is a linear model check, not nonlinear or hardware stabilization.",
    "mode": "input",
    "skill_id": "pythonrobotics-pendulum-lqr",
    "bad_skill_id": "pythonrobotics-pendulum-lqr",
    "runtime": "pythonrobotics",
    "source_commit": "cdd0cc888802b584c2d654ca85e3c5460973487d",
    "bad_input": {
      "state": [
        0,
        0,
        0.1,
        0
      ],
      "state_cost_diagonal": [
        0.0,
        1.0,
        10.0,
        1.0
      ],
      "force_cost": 1.0
    },
    "good_input": {
      "state": [
        0,
        0,
        0.1,
        0
      ],
      "state_cost_diagonal": [
        1.0,
        1.0,
        10.0,
        1.0
      ],
      "force_cost": 1.0
    },
    "contracts_sha256": "cbd174dd17f0bb5fe4b99a98e1fb6f7363a905c03853172fa3e96acb0712f911",
    "scope": "Authored reproduction; not autonomous agent discovery, general accuracy or hardware verification."
  },
  "evidence": {
    "id": "pendulum-lqr-state-cost",
    "case_sha256": "3076abfdbe2d3c989697142a147de90c9e7a9bc07cca880a8494d225089b1d33",
    "problem_reproduced": true,
    "valid_case_passed": true,
    "observed_problem": {
      "exit_code": 2,
      "reason_code": "invalid_input_or_environment"
    },
    "checks": {
      "stable_discrete_linear_model": true
    },
    "adapter_sha256": "cd4c5191e0c4396cebfdd0a70cb4dad95a0c4bccbe288ef08d1feb8ef5206616",
    "bad_adapter_sha256": "cd4c5191e0c4396cebfdd0a70cb4dad95a0c4bccbe288ef08d1feb8ef5206616",
    "source_commit": "cdd0cc888802b584c2d654ca85e3c5460973487d",
    "mode": "input"
  },
  "observed_at": "2026-10-20T04:15:40.176754+00:00",
  "maintainer_authored": true
}