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          "url": "https://raw.githubusercontent.com/Perkins729/OmniXtreme/6f2716d1b1d1132d513d86beb2193f5b18f11fcd/policy/base_policy_trt.onnx"
        },
        {
          "path": "policy/fk_trt.onnx",
          "bytes": 40618,
          "git_blob_sha1": "df1aaa38ee38ff0694537cc2ccfc7ea37adf69db",
          "sha256": "16c1c04d431a15964db422180039c2d0fb688eeb246d0bdbc041de9a25d756ef",
          "url": "https://raw.githubusercontent.com/Perkins729/OmniXtreme/6f2716d1b1d1132d513d86beb2193f5b18f11fcd/policy/fk_trt.onnx"
        },
        {
          "path": "policy/residual_policy.onnx",
          "bytes": 3555934,
          "git_blob_sha1": "48e34f42c7c0685be3a2bcfe8f5a750e4175e842",
          "sha256": "6174205a430b84ec51d3f15e014907504a9ad0c38575535a509bc771ebbc1d85",
          "url": "https://raw.githubusercontent.com/Perkins729/OmniXtreme/6f2716d1b1d1132d513d86beb2193f5b18f11fcd/policy/residual_policy.onnx"
        }
      ]
    },
    "author_source": "https://github.com/Perkins729/OmniXtreme",
    "author_commit": "6f2716d1b1d1132d513d86beb2193f5b18f11fcd",
    "base_adapter_commit": "a1e66b16e1fa8366f115181af9c6f578f18999c2",
    "production_source_sha256": {
      "adapter.py": "f0b660ac158a2356b7dc79e122b632a7710d823c1a4e763d535ced067efa4566",
      "evaluate.py": "0ebb8c761f00dd963f2e45c85ecb94628273911bb1edfc0f2621e8fc458cb4ff",
      "native-sources.json": "4fdfcf8f231d79686fcc26f50cc68f5e906cb0bcf0cfa6c17f32ab742ca221f6",
      "native_rotations.py": "c2f25c9bda700f90e7f048ee34d5b21f0acd37fb3377b5903ee7ce4c5591b339",
      "policy.json": "ac2c390c351ca3de88e637fe794cfa25281284b34a2eae748235620768396ed5",
      "validate_inference.py": "092246cd705db6f6705cecd617bc0bf8a013061e250cb7a0d33b5fba771ebf04",
      "restricted.py": "8f8fdcc4dc41813cf5d8f8c55e4b219310145f74a540457b52c41843a49bd831",
      "parallel.py": "24f5d3b68572d209c8570f8e346ad63b158aac17a86950aebf49c397d2d234fc"
    },
    "prior_native_parity_sha256": {
      "validation/native-parity.json": "8406db109ad4529c873d503e3a1d6e96bfe7c49bdfced33b64f16f7268f580a1"
    },
    "handling": "Released actor uses joint state, angular velocity/gravity or orientation and reference features. Retain author normalization, history/recurrent state, action processing and declared lookahead; validate numerical independence from actual translation.",
    "actual_position_integration": "No explicit integration or odometry in the inspected deployed adapter source; blocked paths are listed separately.",
    "special_handling": "FK of allowed state at fixed synthetic origin (0,0,0); never actual measured origin.",
    "repository": "https://github.com/MotionChallenge-Project/MotionChallenge-omnixtreme",
    "dependency_test": {
      "path": "validation/observation-access.json",
      "sha256": "03794f522b1b79b3c6dac91c3e414c0d47b538959a66fef18c8992c70a305a0f",
      "code": "validate_observations.py",
      "cases": 160,
      "resets": 4
    },
    "execution_validation": {
      "path": "validation/concurrency.json",
      "sha256": "e1bf22cc2a88b28ec18b8b6204f164caad2a40e1ee90d182b505e8ef7f584e9f",
      "scope": "Native outputs, independent histories/RNG, reordered slots/partial resets and all40 two-second closed-loop exact comparison; does not replace full benchmark evidence."
    }
  },
  "observation_dependency_report": {
    "model": "omnixtreme",
    "status": "passed",
    "cases": 160,
    "resets": 4,
    "tolerance_abs": 2e-05,
    "checkpoint_sha256": "b050772c0b49c813f457cdf21bb3e4f23a027e2e22c5279ab23423ea264007e2",
    "network_calls": 480,
    "max_abs_error": {
      "observation": 0.0,
      "network_output": 0.0,
      "motor_command": 0.0
    },
    "checks": [
      "160 matched state sequences / 4 resets",
      "large actual translation perturbations",
      "reference windows preserved including endpoints",
      "all captured ONNX inputs and outputs",
      "all PD command fields",
      "forbidden-field counterfactual before transport"
    ],
    "limits": "Finite numerical regression plus source audit; not a proof against malicious code or implicit neural odometry."
  },
  "reproduction": {
    "adapter_commit": "ad7539f74fe8399ba019f82b4aada1167d0b11c2",
    "core_commit": "da0aec4023bbc3d29be67e1fce9fa9ab8a48c4b5",
    "adapter_repository": "https://github.com/MotionChallenge-Project/MotionChallenge-omnixtreme",
    "adapter_code_url": "https://github.com/MotionChallenge-Project/MotionChallenge-omnixtreme/tree/ad7539f74fe8399ba019f82b4aada1167d0b11c2",
    "core_code_url": "https://github.com/MotionChallenge-Project/MotionChallenge-Core/tree/da0aec4023bbc3d29be67e1fce9fa9ab8a48c4b5",
    "checkpoint_sha256": "b050772c0b49c813f457cdf21bb3e4f23a027e2e22c5279ab23423ea264007e2",
    "commands": [
      "git clone --recurse-submodules https://github.com/MotionChallenge-Project/MotionChallenge-omnixtreme.git",
      "cd MotionChallenge-omnixtreme",
      "uv sync",
      "uv run evaluate.py"
    ],
    "scoring_core_commit": "b08ef12c6898ee866a81bdce055099ee2d0cee4b",
    "scoring_code_url": "https://github.com/MotionChallenge-Project/MotionChallenge-Core/tree/b08ef12c6898ee866a81bdce055099ee2d0cee4b",
    "rescore_command": "uv run motionchallenge-rescore --run /path/to/original/run --original-protocol /path/to/original-protocol.json --data /path/to/dataset --output /path/to/new-score --jobs 8"
  }
}