{
  "schema_version": 2,
  "captured_at_kst": "2026-10-08 11:51:24 KST",
  "records": {
    "repo_loader": {
      "id": "repo_loader",
      "title": "실제 MuQ 적재 경로",
      "claim": "MuQMuLan 적재 객체에서 내부 MuQ 인코더를 선택합니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/src/raina_laya/features/audio_features/infrastructure/features.py",
      "source_path": "src/raina_laya/features/audio_features/infrastructure/features.py",
      "sha256": "aec9c954ee2d06e43fb968632b7e1e420fb637c30059504197ec0c4daa6c1c17",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 26,
          "end_line": 33,
          "lines": [
            {
              "line": 26,
              "source": "BACKBONE: Final = MuQBackbone("
            },
            {
              "line": 27,
              "source": "    repository=\"OpenMuQ/MuQ-MuLan-large\","
            },
            {
              "line": 28,
              "source": "    revision=\"2e01c796b71dca71b45251384c04cd7b237c9020\","
            },
            {
              "line": 29,
              "source": "    inner_path=\"model.mulan.audio.model\","
            },
            {
              "line": 30,
              "source": "    layer_order=(10, 9),"
            },
            {
              "line": 31,
              "source": ")"
            },
            {
              "line": 32,
              "source": "EXPECTED_HIDDEN_WIDTH: Final = 1024"
            },
            {
              "line": 33,
              "source": "HIDDEN_STATE_COUNT: Final = 13"
            }
          ]
        },
        {
          "start_line": 482,
          "end_line": 502,
          "lines": [
            {
              "line": 482,
              "source": "def _default_loader("
            },
            {
              "line": 483,
              "source": "    repository: str,"
            },
            {
              "line": 484,
              "source": "    *,"
            },
            {
              "line": 485,
              "source": "    revision: str,"
            },
            {
              "line": 486,
              "source": "    local_files_only: bool = False,"
            },
            {
              "line": 487,
              "source": ") -> LoadedBackbone:"
            },
            {
              "line": 488,
              "source": "    module = import_module(\"muq\")"
            },
            {
              "line": 489,
              "source": "    root = module.MuQMuLan.from_pretrained("
            },
            {
              "line": 490,
              "source": "        repository,"
            },
            {
              "line": 491,
              "source": "        revision=revision,"
            },
            {
              "line": 492,
              "source": "        local_files_only=local_files_only,"
            },
            {
              "line": 493,
              "source": "    )"
            },
            {
              "line": 494,
              "source": "    inner_model = root.mulan.audio.model"
            },
            {
              "line": 495,
              "source": "    if not isinstance(inner_model, torch.nn.Module):"
            },
            {
              "line": 496,
              "source": "        raise HiddenStateContractError("
            },
            {
              "line": 497,
              "source": "            reason=\"model.mulan.audio.model must be a torch module\","
            },
            {
              "line": 498,
              "source": "        )"
            },
            {
              "line": 499,
              "source": "    return LoadedBackbone("
            },
            {
              "line": 500,
              "source": "        root=root,"
            },
            {
              "line": 501,
              "source": "        inner_model=_InnerModelAdapter(model=inner_model),"
            },
            {
              "line": 502,
              "source": "    )"
            }
          ]
        },
        {
          "start_line": 505,
          "end_line": 535,
          "lines": [
            {
              "line": 505,
              "source": "def load_real_inner_model("
            },
            {
              "line": 506,
              "source": "    loader: MuQLoader = _default_loader,"
            },
            {
              "line": 507,
              "source": "    *,"
            },
            {
              "line": 508,
              "source": "    local_files_only: bool = False,"
            },
            {
              "line": 509,
              "source": ") -> InnerAudioModel:"
            },
            {
              "line": 510,
              "source": "    \"\"\"Load and freeze the pinned inner model on one of four CUDA devices.\"\"\""
            },
            {
              "line": 511,
              "source": "    if not torch.cuda.is_available():"
            },
            {
              "line": 512,
              "source": "        raise CudaExtractionRequiredError(reason=\"CUDA is unavailable\")"
            },
            {
              "line": 513,
              "source": "    visible_devices = os.environ.get(\"CUDA_VISIBLE_DEVICES\")"
            },
            {
              "line": 514,
              "source": "    if visible_devices not in ALLOWED_EXTRACTION_DEVICES:"
            },
            {
              "line": 515,
              "source": "        raise CudaExtractionRequiredError("
            },
            {
              "line": 516,
              "source": "            reason=f\"CUDA_VISIBLE_DEVICES is {visible_devices!r}\","
            },
            {
              "line": 517,
              "source": "        )"
            },
            {
              "line": 518,
              "source": ""
            },
            {
              "line": 519,
              "source": "    loaded = loader("
            },
            {
              "line": 520,
              "source": "        BACKBONE.repository,"
            },
            {
              "line": 521,
              "source": "        revision=BACKBONE.revision,"
            },
            {
              "line": 522,
              "source": "        local_files_only=local_files_only,"
            },
            {
              "line": 523,
              "source": "    )"
            },
            {
              "line": 524,
              "source": "    target = ("
            },
            {
              "line": 525,
              "source": "        loaded.inner_model.model"
            },
            {
              "line": 526,
              "source": "        if isinstance(loaded.inner_model, _InnerModelAdapter)"
            },
            {
              "line": 527,
              "source": "        else loaded.root"
            },
            {
              "line": 528,
              "source": "    )"
            },
            {
              "line": 529,
              "source": "    # Injected legacy test loaders may expose a callable rather than a module."
            },
            {
              "line": 530,
              "source": "    # The real pinned adapter retains only its exact inner audio module."
            },
            {
              "line": 531,
              "source": "    _ = target.eval()"
            },
            {
              "line": 532,
              "source": "    _ = target.to(device=\"cuda\", dtype=torch.float32)"
            },
            {
              "line": 533,
              "source": "    for parameter in target.parameters():"
            },
            {
              "line": 534,
              "source": "        parameter.requires_grad = False"
            },
            {
              "line": 535,
              "source": "    return loaded.inner_model"
            }
          ]
        }
      ]
    },
    "raw_frame_call": {
      "id": "raw_frame_call",
      "title": "MuQ 시간별 숨은 상태",
      "claim": "내부 인코더를 직접 호출해 hidden_states를 읽습니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/src/raina_laya/features/audio_features/infrastructure/features.py",
      "source_path": "src/raina_laya/features/audio_features/infrastructure/features.py",
      "sha256": "aec9c954ee2d06e43fb968632b7e1e420fb637c30059504197ec0c4daa6c1c17",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 417,
          "end_line": 437,
          "lines": [
            {
              "line": 417,
              "source": "def extract_inner_model_layer_frames("
            },
            {
              "line": 418,
              "source": "    model: InnerAudioModel,"
            },
            {
              "line": 419,
              "source": "    context: AudioContext,"
            },
            {
              "line": 420,
              "source": "    *,"
            },
            {
              "line": 421,
              "source": "    window_start: float,"
            },
            {
              "line": 422,
              "source": "    expected_width: int = EXPECTED_HIDDEN_WIDTH,"
            },
            {
              "line": 423,
              "source": ") -> LayerFrames:"
            },
            {
              "line": 424,
              "source": "    \"\"\"Run one context once and retain every hidden state with its alignment.\"\"\""
            },
            {
              "line": 425,
              "source": "    with torch.autocast(device_type=context.waveform.device.type, enabled=False):"
            },
            {
              "line": 426,
              "source": "        output = model("
            },
            {
              "line": 427,
              "source": "            context.waveform.unsqueeze(0).to(dtype=torch.float32),"
            },
            {
              "line": 428,
              "source": "            output_hidden_states=True,"
            },
            {
              "line": 429,
              "source": "        )"
            },
            {
              "line": 430,
              "source": "    layers = _all_hidden_states(output.hidden_states, expected_width)"
            },
            {
              "line": 431,
              "source": "    frame_timestamps, valid_frame_mask = _frame_alignment("
            },
            {
              "line": 432,
              "source": "        layers[0].shape[1], layers[0].device, context, window_start"
            },
            {
              "line": 433,
              "source": "    )"
            },
            {
              "line": 434,
              "source": "    return LayerFrames("
            },
            {
              "line": 435,
              "source": "        layers=layers,"
            },
            {
              "line": 436,
              "source": "        frame_timestamps=frame_timestamps,"
            },
            {
              "line": 437,
              "source": "        valid_frame_mask=valid_frame_mask,"
            }
          ]
        }
      ]
    },
    "bin_pooling": {
      "id": "bin_pooling",
      "title": "층별 평균과 연결",
      "claim": "시간 구간 안에서 층별 평균을 구하고 선택 순서대로 연결합니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/src/raina_laya/features/audio_features/infrastructure/features.py",
      "source_path": "src/raina_laya/features/audio_features/infrastructure/features.py",
      "sha256": "aec9c954ee2d06e43fb968632b7e1e420fb637c30059504197ec0c4daa6c1c17",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 243,
          "end_line": 260,
          "lines": [
            {
              "line": 243,
              "source": "    pooled: list[torch.Tensor] = []"
            },
            {
              "line": 244,
              "source": "    for interval in bins:"
            },
            {
              "line": 245,
              "source": "        selected = ("
            },
            {
              "line": 246,
              "source": "            valid_frame_mask"
            },
            {
              "line": 247,
              "source": "            & (frame_timestamps >= interval.start)"
            },
            {
              "line": 248,
              "source": "            & (frame_timestamps < interval.end)"
            },
            {
              "line": 249,
              "source": "        )"
            },
            {
              "line": 250,
              "source": "        if not torch.any(selected):"
            },
            {
              "line": 251,
              "source": "            raise EmptyPoolingBinError(start=interval.start, end=interval.end)"
            },
            {
              "line": 252,
              "source": "        pooled.append("
            },
            {
              "line": 253,
              "source": "            torch.cat("
            },
            {
              "line": 254,
              "source": "                ("
            },
            {
              "line": 255,
              "source": "                    layer_10[0, selected].mean(dim=0),"
            },
            {
              "line": 256,
              "source": "                    layer_9[0, selected].mean(dim=0),"
            },
            {
              "line": 257,
              "source": "                ),"
            },
            {
              "line": 258,
              "source": "            ),"
            },
            {
              "line": 259,
              "source": "        )"
            },
            {
              "line": 260,
              "source": "    return torch.stack(pooled).to(dtype=torch.float32)"
            }
          ]
        },
        {
          "start_line": 464,
          "end_line": 479,
          "lines": [
            {
              "line": 464,
              "source": "    pooled: list[torch.Tensor] = []"
            },
            {
              "line": 465,
              "source": "    for interval in bins:"
            },
            {
              "line": 466,
              "source": "        selected = ("
            },
            {
              "line": 467,
              "source": "            frames.valid_frame_mask"
            },
            {
              "line": 468,
              "source": "            & (frames.frame_timestamps >= interval.start)"
            },
            {
              "line": 469,
              "source": "            & (frames.frame_timestamps < interval.end)"
            },
            {
              "line": 470,
              "source": "        )"
            },
            {
              "line": 471,
              "source": "        rows = selected.nonzero().squeeze(1)"
            },
            {
              "line": 472,
              "source": "        if rows.numel() == 0:"
            },
            {
              "line": 473,
              "source": "            raise EmptyPoolingBinError(start=interval.start, end=interval.end)"
            },
            {
              "line": 474,
              "source": "        pooled.append("
            },
            {
              "line": 475,
              "source": "            torch.stack("
            },
            {
              "line": 476,
              "source": "                [layer[0].index_select(0, rows).mean(dim=0) for layer in frames.layers],"
            },
            {
              "line": 477,
              "source": "            ),"
            },
            {
              "line": 478,
              "source": "        )"
            },
            {
              "line": 479,
              "source": "    return torch.stack(pooled).to(dtype=torch.float32)"
            }
          ]
        }
      ]
    },
    "feature_width_contract": {
      "id": "feature_width_contract",
      "title": "입력 차원 계약",
      "claim": "판단부 입력은 선택한 MuQ 층의 수에 따라 달라집니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/src/raina_laya/features/grade_model/domain/model_inputs.py",
      "source_path": "src/raina_laya/features/grade_model/domain/model_inputs.py",
      "sha256": "aead5f666afe637244e2db195beb159a60fa9b1c8b05427eb04f369c7230ba56",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 8,
          "end_line": 10,
          "lines": [
            {
              "line": 8,
              "source": "FEATURE_WIDTH: Final = 2048"
            },
            {
              "line": 9,
              "source": "LAYER_WIDTH: Final = 1024"
            },
            {
              "line": 10,
              "source": "LAYER_COUNT: Final = 13"
            }
          ]
        }
      ]
    },
    "config_feature_width": {
      "id": "config_feature_width",
      "title": "설정에서 정하는 입력 폭",
      "claim": "층 선택 설정과 판단부 입력 폭을 연결합니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/src/raina_laya/features/grade_model/application/v4_training_types.py",
      "source_path": "src/raina_laya/features/grade_model/application/v4_training_types.py",
      "sha256": "c854915a1de881b0643e9232bf6f27950633c2f5faf4449e47b1af6fb37b48f0",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 98,
          "end_line": 100,
          "lines": [
            {
              "line": 98,
              "source": "    embedding_layers: tuple[int, ...] | None = None"
            },
            {
              "line": 99,
              "source": "    gate_soft_target_mix: float | None = None"
            },
            {
              "line": 100,
              "source": "    gate_soft_targets_sha256: str | None = None"
            }
          ]
        },
        {
          "start_line": 106,
          "end_line": 111,
          "lines": [
            {
              "line": 106,
              "source": "    @property"
            },
            {
              "line": 107,
              "source": "    def feature_width(self) -> int:"
            },
            {
              "line": 108,
              "source": "        \"\"\"Return the head input width: the legacy L10+L9 cache or chosen layers.\"\"\""
            },
            {
              "line": 109,
              "source": "        if self.embedding_layers is None:"
            },
            {
              "line": 110,
              "source": "            return FEATURE_WIDTH"
            },
            {
              "line": 111,
              "source": "        return LAYER_WIDTH * len(self.embedding_layers)"
            }
          ]
        }
      ]
    },
    "judgment_tokens": {
      "id": "judgment_tokens",
      "title": "시간 트랜스포머 판단부",
      "claim": "음악 토큰 앞에 세 질문 토큰을 넣고 질문 출력만 점수화합니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/src/raina_laya/features/grade_model/domain/model.py",
      "source_path": "src/raina_laya/features/grade_model/domain/model.py",
      "sha256": "ca8996df129dcffe6ebd3cf1a69d3bd20b18e69c31b633b1127a1ad5c7a777a1",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 111,
          "end_line": 164,
          "lines": [
            {
              "line": 111,
              "source": "        self.model_width = model_width"
            },
            {
              "line": 112,
              "source": "        self.feature_width = feature_width"
            },
            {
              "line": 113,
              "source": "        self.transformer_depth = transformer_depth"
            },
            {
              "line": 114,
              "source": "        self.use_time_encoding = use_time_encoding"
            },
            {
              "line": 115,
              "source": "        self.feature_projection: nn.Linear = nn.Linear(feature_width, model_width)"
            },
            {
              "line": 116,
              "source": "        self.projection_norm: nn.LayerNorm = nn.LayerNorm(model_width)"
            },
            {
              "line": 117,
              "source": "        self.ratio_embedding: nn.Linear = nn.Linear(1, model_width)"
            },
            {
              "line": 118,
              "source": "        self.grade_tokens = nn.Parameter(torch.empty(GRADE_COUNT, model_width))"
            },
            {
              "line": 119,
              "source": "        self.type_embedding: nn.Embedding = nn.Embedding(2, model_width)"
            },
            {
              "line": 120,
              "source": "        self.first_transformer_layer: nn.TransformerEncoderLayer = ("
            },
            {
              "line": 121,
              "source": "            nn.TransformerEncoderLayer("
            },
            {
              "line": 122,
              "source": "                d_model=model_width,"
            },
            {
              "line": 123,
              "source": "                nhead=4,"
            },
            {
              "line": 124,
              "source": "                dim_feedforward=model_width * 4,"
            },
            {
              "line": 125,
              "source": "                dropout=transformer_dropout,"
            },
            {
              "line": 126,
              "source": "                activation=\"gelu\","
            },
            {
              "line": 127,
              "source": "                batch_first=True,"
            },
            {
              "line": 128,
              "source": "                norm_first=True,"
            },
            {
              "line": 129,
              "source": "            )"
            },
            {
              "line": 130,
              "source": "        )"
            },
            {
              "line": 131,
              "source": "        self.second_transformer_layer: nn.TransformerEncoderLayer | None = ("
            },
            {
              "line": 132,
              "source": "            nn.TransformerEncoderLayer("
            },
            {
              "line": 133,
              "source": "                d_model=model_width,"
            },
            {
              "line": 134,
              "source": "                nhead=4,"
            },
            {
              "line": 135,
              "source": "                dim_feedforward=model_width * 4,"
            },
            {
              "line": 136,
              "source": "                dropout=transformer_dropout,"
            },
            {
              "line": 137,
              "source": "                activation=\"gelu\","
            },
            {
              "line": 138,
              "source": "                batch_first=True,"
            },
            {
              "line": 139,
              "source": "                norm_first=True,"
            },
            {
              "line": 140,
              "source": "            )"
            },
            {
              "line": 141,
              "source": "            if transformer_depth >= DEEP_TRANSFORMER_DEPTH"
            },
            {
              "line": 142,
              "source": "            else None"
            },
            {
              "line": 143,
              "source": "        )"
            },
            {
              "line": 144,
              "source": "        self.third_transformer_layer: nn.TransformerEncoderLayer | None = ("
            },
            {
              "line": 145,
              "source": "            nn.TransformerEncoderLayer("
            },
            {
              "line": 146,
              "source": "                d_model=model_width,"
            },
            {
              "line": 147,
              "source": "                nhead=4,"
            },
            {
              "line": 148,
              "source": "                dim_feedforward=model_width * 4,"
            },
            {
              "line": 149,
              "source": "                dropout=transformer_dropout,"
            },
            {
              "line": 150,
              "source": "                activation=\"gelu\","
            },
            {
              "line": 151,
              "source": "                batch_first=True,"
            },
            {
              "line": 152,
              "source": "                norm_first=True,"
            },
            {
              "line": 153,
              "source": "            )"
            },
            {
              "line": 154,
              "source": "            if transformer_depth == THIRD_TRANSFORMER_DEPTH"
            },
            {
              "line": 155,
              "source": "            else None"
            },
            {
              "line": 156,
              "source": "        )"
            },
            {
              "line": 157,
              "source": "        self.scorer: nn.Sequential = nn.Sequential("
            },
            {
              "line": 158,
              "source": "            nn.LayerNorm(model_width),"
            },
            {
              "line": 159,
              "source": "            nn.Linear(model_width, model_width),"
            },
            {
              "line": 160,
              "source": "            nn.GELU(),"
            },
            {
              "line": 161,
              "source": "            nn.Linear(model_width, 1, bias=False),"
            },
            {
              "line": 162,
              "source": "        )"
            },
            {
              "line": 163,
              "source": "        _ = nn.init.normal_(self.grade_tokens, mean=0.0, std=0.02)"
            },
            {
              "line": 164,
              "source": "        self.conditional_scorer: nn.Sequential | None = ("
            }
          ]
        },
        {
          "start_line": 191,
          "end_line": 250,
          "lines": [
            {
              "line": 191,
              "source": "    @override"
            },
            {
              "line": 192,
              "source": "    def forward("
            },
            {
              "line": 193,
              "source": "        self,"
            },
            {
              "line": 194,
              "source": "        features: torch.Tensor,"
            },
            {
              "line": 195,
              "source": "        center_times: torch.Tensor,"
            },
            {
              "line": 196,
              "source": "        valid_ratios: torch.Tensor,"
            },
            {
              "line": 197,
              "source": "        padding_mask: torch.Tensor,"
            },
            {
              "line": 198,
              "source": "    ) -> torch.Tensor:"
            },
            {
              "line": 199,
              "source": "        \"\"\"Return three raw query scores, interpreted by the model family."
            },
            {
              "line": 200,
              "source": ""
            },
            {
              "line": 201,
              "source": "        Optional detachment blocks only direct conditional-loss encoder gradients."
            },
            {
              "line": 202,
              "source": "        Global gradient clipping can still couple the subsequent optimizer updates."
            },
            {
              "line": 203,
              "source": "        \"\"\""
            },
            {
              "line": 204,
              "source": "        self._validate_inputs("
            },
            {
              "line": 205,
              "source": "            features,"
            },
            {
              "line": 206,
              "source": "            center_times,"
            },
            {
              "line": 207,
              "source": "            valid_ratios,"
            },
            {
              "line": 208,
              "source": "            padding_mask,"
            },
            {
              "line": 209,
              "source": "            self.feature_width,"
            },
            {
              "line": 210,
              "source": "        )"
            },
            {
              "line": 211,
              "source": "        batch_size = features.shape[0]"
            },
            {
              "line": 212,
              "source": "        normalized: torch.Tensor = functional.normalize(features, p=2.0, dim=-1)"
            },
            {
              "line": 213,
              "source": "        music_tokens: torch.Tensor = self.projection_norm("
            },
            {
              "line": 214,
              "source": "            self.feature_projection(normalized),"
            },
            {
              "line": 215,
              "source": "        )"
            },
            {
              "line": 216,
              "source": "        if self.magnitude_residual is not None:"
            },
            {
              "line": 217,
              "source": "            music_tokens = self.magnitude_residual(features, music_tokens, padding_mask)"
            },
            {
              "line": 218,
              "source": "        if self.local_temporal_residual is not None:"
            },
            {
              "line": 219,
              "source": "            music_tokens = self.local_temporal_residual(music_tokens, padding_mask)"
            },
            {
              "line": 220,
              "source": "        if self.use_time_encoding:"
            },
            {
              "line": 221,
              "source": "            music_tokens = music_tokens + self._sinusoidal_time_encoding("
            },
            {
              "line": 222,
              "source": "                center_times, self.model_width"
            },
            {
              "line": 223,
              "source": "            )"
            },
            {
              "line": 224,
              "source": "        music_tokens = ("
            },
            {
              "line": 225,
              "source": "            music_tokens"
            },
            {
              "line": 226,
              "source": "            + self.ratio_embedding(valid_ratios.unsqueeze(-1))"
            },
            {
              "line": 227,
              "source": "            + self.type_embedding.weight[MUSIC_TYPE_INDEX]"
            },
            {
              "line": 228,
              "source": "        )"
            },
            {
              "line": 229,
              "source": "        grade_tokens = self.grade_tokens.unsqueeze(0).expand(batch_size, -1, -1)"
            },
            {
              "line": 230,
              "source": "        grade_tokens = grade_tokens + self.type_embedding.weight[GRADE_TYPE_INDEX]"
            },
            {
              "line": 231,
              "source": "        sequence: torch.Tensor = torch.cat((grade_tokens, music_tokens), dim=1)"
            },
            {
              "line": 232,
              "source": "        grade_mask = torch.zeros("
            },
            {
              "line": 233,
              "source": "            (batch_size, GRADE_COUNT),"
            },
            {
              "line": 234,
              "source": "            dtype=torch.bool,"
            },
            {
              "line": 235,
              "source": "            device=padding_mask.device,"
            },
            {
              "line": 236,
              "source": "        )"
            },
            {
              "line": 237,
              "source": "        key_padding_mask = torch.cat((grade_mask, padding_mask), dim=1)"
            },
            {
              "line": 238,
              "source": "        for layer in self.transformer_layers:"
            },
            {
              "line": 239,
              "source": "            sequence = layer("
            },
            {
              "line": 240,
              "source": "                sequence,"
            },
            {
              "line": 241,
              "source": "                src_key_padding_mask=key_padding_mask,"
            },
            {
              "line": 242,
              "source": "            )"
            },
            {
              "line": 243,
              "source": "        logits: torch.Tensor = self.scorer(sequence[:, :GRADE_COUNT])"
            },
            {
              "line": 244,
              "source": "        if self.conditional_scorer is not None:"
            },
            {
              "line": 245,
              "source": "            conditional_features = sequence[:, 2:3]"
            },
            {
              "line": 246,
              "source": "            if self.detach_conditional_features:"
            },
            {
              "line": 247,
              "source": "                conditional_features = conditional_features.detach()"
            },
            {
              "line": 248,
              "source": "            conditional: torch.Tensor = self.conditional_scorer(conditional_features)"
            },
            {
              "line": 249,
              "source": "            logits = torch.cat((logits[:, :2], conditional), dim=1)"
            },
            {
              "line": 250,
              "source": "        return logits.squeeze(-1)"
            }
          ]
        }
      ]
    },
    "cache_route": {
      "id": "cache_route",
      "title": "층별 특징 캐시 선택",
      "claim": "명시적 층 선택이 있으면 층별 저장소를 이용합니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/src/raina_laya/workflows/training_inputs.py",
      "source_path": "src/raina_laya/workflows/training_inputs.py",
      "sha256": "b017d6c69bfcf1c7ff28a7f69b8c011f7cfa0b748a2ca163007beeb3b7710f4b",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 311,
          "end_line": 325,
          "lines": [
            {
              "line": 311,
              "source": "        if self.layer_dir is not None:"
            },
            {
              "line": 312,
              "source": "            source_hashes, inventory_digest = _source_inventory("
            },
            {
              "line": 313,
              "source": "                self.cache_inventory_path,"
            },
            {
              "line": 314,
              "source": "                expected_sources,"
            },
            {
              "line": 315,
              "source": "            )"
            },
            {
              "line": 316,
              "source": "            try:"
            },
            {
              "line": 317,
              "source": "                store = self.audio_features.open_layer_stack("
            },
            {
              "line": 318,
              "source": "                    self.layer_dir,"
            },
            {
              "line": 319,
              "source": "                    self.layers,"
            },
            {
              "line": 320,
              "source": "                )"
            },
            {
              "line": 321,
              "source": "            except CacheIncompleteError as error:"
            },
            {
              "line": 322,
              "source": "                raise TrainingRunInputError("
            },
            {
              "line": 323,
              "source": "                    detail=f\"layer store is missing: {error.path}\","
            },
            {
              "line": 324,
              "source": "                ) from error"
            },
            {
              "line": 325,
              "source": "            inventory_digest = store.lineage_digest(inventory_digest)"
            }
          ]
        },
        {
          "start_line": 337,
          "end_line": 344,
          "lines": [
            {
              "line": 337,
              "source": "        else:"
            },
            {
              "line": 338,
              "source": "            source_hashes, inventory_digest = _source_inventory("
            },
            {
              "line": 339,
              "source": "                self.cache_inventory_path,"
            },
            {
              "line": 340,
              "source": "                expected_sources,"
            },
            {
              "line": 341,
              "source": "            )"
            },
            {
              "line": 342,
              "source": "            if not self.embedding_database.is_file():"
            },
            {
              "line": 343,
              "source": "                raise TrainingRunInputError(detail=\"embedding database is missing\")"
            },
            {
              "line": 344,
              "source": "            store = self.audio_features.open_sqlite_store(self.embedding_database)"
            }
          ]
        }
      ]
    },
    "installed_wrapper_loader": {
      "id": "installed_wrapper_loader",
      "title": "설치된 MuQ 구성 코드",
      "claim": "MuQ 계열을 선택하면 MuQ.from_pretrained(model_name)를 호출합니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/.venv/lib/python3.13/site-packages/muq/muq_mulan/models/audio.py",
      "source_path": ".venv/lib/python3.13/site-packages/muq/muq_mulan/models/audio.py",
      "sha256": "05231129cffeb5132aded81b9aabc79ee29fc01513f26879ef98b1b0fbb14b9c",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 240,
          "end_line": 243,
          "lines": [
            {
              "line": 240,
              "source": "    def _init_pretrained_model(self, model_name):"
            },
            {
              "line": 241,
              "source": "        if 'muq' in model_name.lower():"
            },
            {
              "line": 242,
              "source": "            from muq import MuQ"
            },
            {
              "line": 243,
              "source": "            self.model = MuQ.from_pretrained(model_name, cache_dir=self.hf_hub_cache_dir)"
            }
          ]
        },
        {
          "start_line": 258,
          "end_line": 261,
          "lines": [
            {
              "line": 258,
              "source": "    def _forward_pretrained_model(self, x):"
            },
            {
              "line": 259,
              "source": "        if 'muq' in self.model_name.lower():"
            },
            {
              "line": 260,
              "source": "            outputs = self.model(x, output_hidden_states=True)"
            },
            {
              "line": 261,
              "source": "            return outputs.hidden_states # 13 layer x [batch_size, Time steps, 1024 feature_dim]"
            }
          ]
        },
        {
          "start_line": 279,
          "end_line": 288,
          "lines": [
            {
              "line": 279,
              "source": "        layer_hidden_states = outputs[self.use_layer_idx]"
            },
            {
              "line": 280,
              "source": ""
            },
            {
              "line": 281,
              "source": "        if no_proj:"
            },
            {
              "line": 282,
              "source": "            outputs = layer_hidden_states"
            },
            {
              "line": 283,
              "source": "        else:"
            },
            {
              "line": 284,
              "source": "            outputs = self.proj(layer_hidden_states)"
            },
            {
              "line": 285,
              "source": "        outputs, layer_results = self.transformer(outputs, return_all_layers=True)"
            },
            {
              "line": 286,
              "source": ""
            },
            {
              "line": 287,
              "source": "        if return_mean:"
            },
            {
              "line": 288,
              "source": "            outputs = outputs.mean(dim = -2)"
            }
          ]
        }
      ]
    },
    "installed_wrapper_construction": {
      "id": "installed_wrapper_construction",
      "title": "MuQMuLan 객체 구성",
      "claim": "MuQ 내부 인코더와 MuLan 투영부가 서로 다른 구성요소임을 확인합니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/.venv/lib/python3.13/site-packages/muq/muq_mulan/muq_mulan.py",
      "source_path": ".venv/lib/python3.13/site-packages/muq/muq_mulan/muq_mulan.py",
      "sha256": "594843bf29efed147f97768c60946d8e247dce183af085fc5dffcd3d03a7164e",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 95,
          "end_line": 104,
          "lines": [
            {
              "line": 95,
              "source": "    def create_MuLan_from_config(cls, config:MuQMuLanConfig, hf_hub_cache_dir=None) -> MuLanModel:"
            },
            {
              "line": 96,
              "source": ""
            },
            {
              "line": 97,
              "source": "        audio_transformer = AudioSpectrogramTransformerPretrained("
            },
            {
              "line": 98,
              "source": "            model_name = config.audio_model.name, "
            },
            {
              "line": 99,
              "source": "            model_dim = config.audio_model.model_dim,"
            },
            {
              "line": 100,
              "source": "            use_layer_idx = config.audio_model.use_layer_idx,"
            },
            {
              "line": 101,
              "source": "            **config.audio_transformer,"
            },
            {
              "line": 102,
              "source": "            frozen_pretrained = False,"
            },
            {
              "line": 103,
              "source": "            hf_hub_cache_dir = hf_hub_cache_dir,"
            },
            {
              "line": 104,
              "source": "        )"
            }
          ]
        }
      ]
    },
    "installed_original_muq": {
      "id": "installed_original_muq",
      "title": "설치된 원본 MuQ 구현",
      "claim": "원본 MuQ 클래스의 구성 및 숨은 상태 반환 경로입니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/.venv/lib/python3.13/site-packages/muq/muq/muq.py",
      "source_path": ".venv/lib/python3.13/site-packages/muq/muq/muq.py",
      "sha256": "dbec360f5e3ef622d83f1a73085e67ddde946eb626acad768cc01c1f81374211",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 19,
          "end_line": 20,
          "lines": [
            {
              "line": 19,
              "source": "    encoder_dim:int = field(default=1024)"
            },
            {
              "line": 20,
              "source": "    encoder_depth:int = field(default=12)"
            }
          ]
        },
        {
          "start_line": 35,
          "end_line": 41,
          "lines": [
            {
              "line": 35,
              "source": "class MuQ(nn.Module, PyTorchModelHubMixin):"
            },
            {
              "line": 36,
              "source": "    def __init__(self, config: MuQConfig):"
            },
            {
              "line": 37,
              "source": "        super().__init__()"
            },
            {
              "line": 38,
              "source": "        if isinstance(config, dict):"
            },
            {
              "line": 39,
              "source": "            config = MuQConfig(**config)"
            },
            {
              "line": 40,
              "source": "        self.config = config"
            },
            {
              "line": 41,
              "source": "        self.model = MuQModel("
            }
          ]
        },
        {
          "start_line": 83,
          "end_line": 90,
          "lines": [
            {
              "line": 83,
              "source": "        _, hidden_states = self.model.get_predictions(x, attention_mask=attention_mask, is_features_only=True)"
            },
            {
              "line": 84,
              "source": "        last_hidden_state = hidden_states[-1]"
            },
            {
              "line": 85,
              "source": "        if not output_hidden_states:"
            },
            {
              "line": 86,
              "source": "            return BaseModelOutput(last_hidden_state=last_hidden_state)"
            },
            {
              "line": 87,
              "source": "        return BaseModelOutput("
            },
            {
              "line": 88,
              "source": "            last_hidden_state=last_hidden_state,"
            },
            {
              "line": 89,
              "source": "            hidden_states=hidden_states"
            },
            {
              "line": 90,
              "source": "        )"
            }
          ]
        }
      ]
    },
    "wrapper_cached_config": {
      "id": "wrapper_cached_config",
      "title": "적재 컨테이너 설정",
      "claim": "audio_model = OpenMuQ/MuQ-large-msd-iter를 확인합니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/model/raina-laya-best-20261001/huggingface/hub/models--OpenMuQ--MuQ-MuLan-large/snapshots/2e01c796b71dca71b45251384c04cd7b237c9020/config.json",
      "source_path": "model/raina-laya-best-20261001/huggingface/hub/models--OpenMuQ--MuQ-MuLan-large/snapshots/2e01c796b71dca71b45251384c04cd7b237c9020/config.json",
      "sha256": "8fefc545ef87ecd9bcde7417dd03464370c48c321f36dcff20266a752079e468",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 12,
          "end_line": 16,
          "lines": [
            {
              "line": 12,
              "source": "  \"audio_model\": {"
            },
            {
              "line": 13,
              "source": "    \"name\": \"OpenMuQ/MuQ-large-msd-iter\","
            },
            {
              "line": 14,
              "source": "    \"model_dim\": 1024,"
            },
            {
              "line": 15,
              "source": "    \"use_layer_idx\": -1"
            },
            {
              "line": 16,
              "source": "  },"
            }
          ]
        }
      ]
    },
    "original_cached_config": {
      "id": "original_cached_config",
      "title": "원본 MuQ 설정",
      "claim": "encoder_dim = 1024, encoder_depth = 12입니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/model/raina-laya-best-20261001/huggingface/hub/models--OpenMuQ--MuQ-large-msd-iter/snapshots/0562a57814f6f8bbd9fdea0a25921a2fce1a841a/config.json",
      "source_path": "model/raina-laya-best-20261001/huggingface/hub/models--OpenMuQ--MuQ-large-msd-iter/snapshots/0562a57814f6f8bbd9fdea0a25921a2fce1a841a/config.json",
      "sha256": "237335ee27d8fb951ce778701a12a79e06c51ae636dd786f97e45f51ce532543",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 5,
          "end_line": 7,
          "lines": [
            {
              "line": 5,
              "source": "  \"encoder_depth\": 12,"
            },
            {
              "line": 6,
              "source": "  \"encoder_dim\": 1024,"
            },
            {
              "line": 7,
              "source": "  \"features\": ["
            }
          ]
        }
      ]
    },
    "hierarchical": {
      "id": "hierarchical",
      "title": "계층 확률·판정·증류 손실",
      "claim": "관문과 조건부 확률을 분해하며, 실제 Pass 곡에만 A/S 손실을 적용합니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/src/raina_laya/features/grade_model/domain/hierarchical.py",
      "source_path": "src/raina_laya/features/grade_model/domain/hierarchical.py",
      "sha256": "107af6d5ed654e5784d5198915a8fdc0b163bdd7b35e073d5aa5b20dcf7ef536",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 22,
          "end_line": 86,
          "lines": [
            {
              "line": 22,
              "source": "def hierarchical_log_probs(logits: torch.Tensor) -> torch.Tensor:"
            },
            {
              "line": 23,
              "source": "    \"\"\"Factor final Fail/A/S probabilities from raw [Fail, Pass, S|Pass] scores.\"\"\""
            },
            {
              "line": 24,
              "source": "    gate = functional.log_softmax(logits[:, :2], dim=1)"
            },
            {
              "line": 25,
              "source": "    h = logits[:, 2]"
            },
            {
              "line": 26,
              "source": "    return torch.stack("
            },
            {
              "line": 27,
              "source": "        ("
            },
            {
              "line": 28,
              "source": "            gate[:, 0],"
            },
            {
              "line": 29,
              "source": "            gate[:, 1] + functional.logsigmoid(-h),"
            },
            {
              "line": 30,
              "source": "            gate[:, 1] + functional.logsigmoid(h),"
            },
            {
              "line": 31,
              "source": "        ),"
            },
            {
              "line": 32,
              "source": "        dim=1,"
            },
            {
              "line": 33,
              "source": "    )"
            },
            {
              "line": 34,
              "source": ""
            },
            {
              "line": 35,
              "source": ""
            },
            {
              "line": 36,
              "source": "def hierarchical_predictions(logits: torch.Tensor) -> torch.Tensor:"
            },
            {
              "line": 37,
              "source": "    \"\"\"Resolve gate ties to Fail and conditional ties to A, never joint argmax.\"\"\""
            },
            {
              "line": 38,
              "source": "    return torch.where("
            },
            {
              "line": 39,
              "source": "        logits[:, 1] > logits[:, 0],"
            },
            {
              "line": 40,
              "source": "        1 + (logits[:, 2] > 0).long(),"
            },
            {
              "line": 41,
              "source": "        0,"
            },
            {
              "line": 42,
              "source": "    )"
            },
            {
              "line": 43,
              "source": ""
            },
            {
              "line": 44,
              "source": ""
            },
            {
              "line": 45,
              "source": "def hierarchical_gate_margin(logits: torch.Tensor) -> torch.Tensor:"
            },
            {
              "line": 46,
              "source": "    \"\"\"Return Pass-minus-Fail gate scores: above zero is Pass, ties stay Fail.\"\"\""
            },
            {
              "line": 47,
              "source": "    return logits[:, 1] - logits[:, 0]"
            },
            {
              "line": 48,
              "source": ""
            },
            {
              "line": 49,
              "source": ""
            },
            {
              "line": 50,
              "source": "def mix_gate_targets("
            },
            {
              "line": 51,
              "source": "    targets: torch.Tensor, teacher_margins: torch.Tensor, mix: float"
            },
            {
              "line": 52,
              "source": ") -> torch.Tensor:"
            },
            {
              "line": 53,
              "source": "    \"\"\"Mix the hard Pass indicator with the teacher gate's Pass probability per song.\"\"\""
            },
            {
              "line": 54,
              "source": "    return (1 - mix) * (targets != 0).to(teacher_margins.dtype) + ("
            },
            {
              "line": 55,
              "source": "        mix * teacher_margins.sigmoid()"
            },
            {
              "line": 56,
              "source": "    )"
            },
            {
              "line": 57,
              "source": ""
            },
            {
              "line": 58,
              "source": ""
            },
            {
              "line": 59,
              "source": "def hierarchical_loss("
            },
            {
              "line": 60,
              "source": "    logits: torch.Tensor,"
            },
            {
              "line": 61,
              "source": "    targets: torch.Tensor,"
            },
            {
              "line": 62,
              "source": "    *,"
            },
            {
              "line": 63,
              "source": "    fail_weight: float = 1.0,"
            },
            {
              "line": 64,
              "source": "    conditional_weight: float = 1.0,"
            },
            {
              "line": 65,
              "source": "    gate_target: torch.Tensor | None = None,"
            },
            {
              "line": 66,
              "source": ") -> torch.Tensor:"
            },
            {
              "line": 67,
              "source": "    \"\"\"Return per-song gate CE plus actual-Pass-masked conditional BCE."
            },
            {
              "line": 68,
              "source": ""
            },
            {
              "line": 69,
              "source": "    `gate_target` replaces only the gate's hard Pass indicator with a per-song soft"
            },
            {
              "line": 70,
              "source": "    Pass probability; Fail weights and the conditional term still follow `targets`."
            },
            {
              "line": 71,
              "source": "    \"\"\""
            },
            {
              "line": 72,
              "source": "    actual_pass = targets != 0"
            },
            {
              "line": 73,
              "source": "    gate = functional.cross_entropy("
            },
            {
              "line": 74,
              "source": "        logits[:, :2],"
            },
            {
              "line": 75,
              "source": "        actual_pass.long()"
            },
            {
              "line": 76,
              "source": "        if gate_target is None"
            },
            {
              "line": 77,
              "source": "        else torch.stack((1 - gate_target, gate_target), dim=1).to(logits.dtype),"
            },
            {
              "line": 78,
              "source": "        reduction=\"none\","
            },
            {
              "line": 79,
              "source": "    )"
            },
            {
              "line": 80,
              "source": "    gate = torch.where(actual_pass, gate, gate * fail_weight)"
            },
            {
              "line": 81,
              "source": "    conditional = functional.binary_cross_entropy_with_logits("
            },
            {
              "line": 82,
              "source": "        logits[:, 2],"
            },
            {
              "line": 83,
              "source": "        (targets == S_INDEX).to(logits.dtype),"
            },
            {
              "line": 84,
              "source": "        reduction=\"none\","
            },
            {
              "line": 85,
              "source": "    )"
            },
            {
              "line": 86,
              "source": "    return gate + conditional_weight * conditional * actual_pass.to(logits.dtype)"
            }
          ]
        }
      ]
    },
    "reject_selection": {
      "id": "reject_selection",
      "title": "거부 구간과 검증 선택 규칙",
      "claim": "실제 Pass 거부 70% 이하 및 판정 곡 Pass→Fail 제약을 두고 두 오류 개수를 순서대로 줄입니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/src/raina_laya/features/grade_model/application/v4_evaluate.py",
      "source_path": "src/raina_laya/features/grade_model/application/v4_evaluate.py",
      "sha256": "cc37695f40e7a1d060788978575bc9020ec88e06ecbc03f4bd943ec8672c4e8c",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 230,
          "end_line": 285,
          "lines": [
            {
              "line": 230,
              "source": "# Share of actual Pass songs a band may reject (user decisions on 2026-10-07: 30%,"
            },
            {
              "line": 231,
              "source": "# then 50%, then 70% at 23:1x KST: \"no Fail->Pass at all comes first\"; see"
            },
            {
              "line": 232,
              "source": "# _Autoresearch/20261007/06_거부_구간_계획.md §7 and AGENTS.md 1-1)."
            },
            {
              "line": 233,
              "source": "MAX_PASS_LOSS: Final = 0.70"
            },
            {
              "line": 234,
              "source": ""
            },
            {
              "line": 235,
              "source": ""
            },
            {
              "line": 236,
              "source": "def select_reject_band("
            },
            {
              "line": 237,
              "source": "    margins: Sequence[float],"
            },
            {
              "line": 238,
              "source": "    targets: Sequence[int],"
            },
            {
              "line": 239,
              "source": "    max_pass_loss: float = MAX_PASS_LOSS,"
            },
            {
              "line": 240,
              "source": "    grid: int = 200,"
            },
            {
              "line": 241,
              "source": ") -> RejectBand:"
            },
            {
              "line": 242,
              "source": "    \"\"\"Choose (lo, hi) on one set's own margins; call it on validation only."
            },
            {
              "line": 243,
              "source": ""
            },
            {
              "line": 244,
              "source": "    Feasible bands reject at most `max_pass_loss` of the actual Pass songs and keep"
            },
            {
              "line": 245,
              "source": "    the decided Pass->Fail rate at or below the no-reject rate at boundary 0 (so a"
            },
            {
              "line": 246,
              "source": "    band never buys Fail->Pass by shifting errors onto Pass->Fail; without this cap"
            },
            {
              "line": 247,
              "source": "    hi = +inf is always \"optimal\"). Among feasible bands: fewest decided Fail->Pass,"
            },
            {
              "line": 248,
              "source": "    then fewest decided Pass->Fail, then fewest rejected songs. Cut points are the"
            },
            {
              "line": 249,
              "source": "    margin quantiles plus 0. Falls back to (0, 0) when nothing is feasible."
            },
            {
              "line": 250,
              "source": "    \"\"\""
            },
            {
              "line": 251,
              "source": "    m = np.asarray(margins, dtype=np.float64)"
            },
            {
              "line": 252,
              "source": "    is_pass = np.asarray(targets) != 0"
            },
            {
              "line": 253,
              "source": "    if m.ndim != 1 or m.shape != is_pass.shape or m.size == 0:"
            },
            {
              "line": 254,
              "source": "        raise EvaluationInputError(detail=\"reject band needs equal-length 1-D inputs\")"
            },
            {
              "line": 255,
              "source": "    pm, fm = np.sort(m[is_pass]), np.sort(m[~is_pass])"
            },
            {
              "line": 256,
              "source": "    if pm.size == 0 or fm.size == 0:"
            },
            {
              "line": 257,
              "source": "        raise EvaluationInputError(detail=\"reject band needs both Fail and Pass songs\")"
            },
            {
              "line": 258,
              "source": "    max_p2f_rate = (pm <= 0.0).sum() / pm.size"
            },
            {
              "line": 259,
              "source": "    cuts = np.unique(np.concatenate([np.quantile(m, np.linspace(0, 1, grid)), [0.0]]))"
            },
            {
              "line": 260,
              "source": "    lo, hi = cuts[:, None], cuts[None, :]"
            },
            {
              "line": 261,
              "source": "    pass_le_lo = np.searchsorted(pm, lo, side=\"right\")  # Pass decided Fail"
            },
            {
              "line": 262,
              "source": "    pass_ge_hi = pm.size - np.searchsorted(pm, hi, side=\"left\")  # Pass decided Pass"
            },
            {
              "line": 263,
              "source": "    fail_ge_hi = fm.size - np.searchsorted(fm, hi, side=\"left\")  # Fail decided Pass"
            },
            {
              "line": 264,
              "source": "    fail_le_lo = np.searchsorted(fm, lo, side=\"right\")"
            },
            {
              "line": 265,
              "source": "    decided_pass = pass_le_lo + pass_ge_hi"
            },
            {
              "line": 266,
              "source": "    rejected = (pm.size - decided_pass) + (fm.size - fail_le_lo - fail_ge_hi)"
            },
            {
              "line": 267,
              "source": "    p2f_rate = np.divide("
            },
            {
              "line": 268,
              "source": "        pass_le_lo,"
            },
            {
              "line": 269,
              "source": "        decided_pass,"
            },
            {
              "line": 270,
              "source": "        out=np.ones(decided_pass.shape),"
            },
            {
              "line": 271,
              "source": "        where=decided_pass > 0,"
            },
            {
              "line": 272,
              "source": "    )"
            },
            {
              "line": 273,
              "source": "    feasible = ("
            },
            {
              "line": 274,
              "source": "        (lo <= hi)"
            },
            {
              "line": 275,
              "source": "        & (pm.size - decided_pass <= max_pass_loss * pm.size + 1e-9)"
            },
            {
              "line": 276,
              "source": "        & (p2f_rate <= max_p2f_rate + 1e-12)"
            },
            {
              "line": 277,
              "source": "    )"
            },
            {
              "line": 278,
              "source": "    if not feasible.any():"
            },
            {
              "line": 279,
              "source": "        return RejectBand(lo=0.0, hi=0.0)"
            },
            {
              "line": 280,
              "source": "    big = 1 << 40"
            },
            {
              "line": 281,
              "source": "    keys = ["
            },
            {
              "line": 282,
              "source": "        np.where(feasible, k, big).ravel() for k in (fail_ge_hi, pass_le_lo, rejected)"
            },
            {
              "line": 283,
              "source": "    ]"
            },
            {
              "line": 284,
              "source": "    i, j = np.unravel_index(np.lexsort(keys[::-1])[0], feasible.shape)"
            },
            {
              "line": 285,
              "source": "    return RejectBand(lo=float(cuts[i]), hi=float(cuts[j]))"
            }
          ]
        },
        {
          "start_line": 315,
          "end_line": 357,
          "lines": [
            {
              "line": 315,
              "source": "def choice_checkpoint_key("
            },
            {
              "line": 316,
              "source": "    checkpoint: ChoiceCheckpoint,"
            },
            {
              "line": 317,
              "source": "    priority: ChoiceSelectionPriority = ChoiceSelectionPriority.GRADE,"
            },
            {
              "line": 318,
              "source": "    minimum_pass_recall: float = 0.79,"
            },
            {
              "line": 319,
              "source": "    *,"
            },
            {
              "line": 320,
              "source": "    maximum_fail_to_pass_rate: float = 0.05,"
            },
            {
              "line": 321,
              "source": ") -> tuple[float, ...]:"
            },
            {
              "line": 322,
              "source": "    \"\"\"Rank validated checkpoints under the explicitly selected research goal.\"\"\""
            },
            {
              "line": 323,
              "source": "    metrics = checkpoint.metrics"
            },
            {
              "line": 324,
              "source": "    macro_f1 = metrics.macro_f1"
            },
            {
              "line": 325,
              "source": "    s_f1 = metrics.per_class[2].f1"
            },
            {
              "line": 326,
              "source": "    nll = metrics.nll"
            },
            {
              "line": 327,
              "source": "    if macro_f1 is None or s_f1 is None or nll is None:"
            },
            {
              "line": 328,
              "source": "        raise CheckpointSelectionError("
            },
            {
              "line": 329,
              "source": "            detail=\"validation needs all three grades to rank checkpoints\""
            },
            {
              "line": 330,
              "source": "        )"
            },
            {
              "line": 331,
              "source": "    match priority:"
            },
            {
              "line": 332,
              "source": "        case ChoiceSelectionPriority.GRADE:"
            },
            {
              "line": 333,
              "source": "            return macro_f1, s_f1, -nll"
            },
            {
              "line": 334,
              "source": "        case ChoiceSelectionPriority.BINARY:"
            },
            {
              "line": 335,
              "source": "            binary = pass_fail_metrics(metrics)"
            },
            {
              "line": 336,
              "source": "            return ("
            },
            {
              "line": 337,
              "source": "                binary.binary_macro_f1,"
            },
            {
              "line": 338,
              "source": "                binary.fail_recall,"
            },
            {
              "line": 339,
              "source": "                binary.pass_recall,"
            },
            {
              "line": 340,
              "source": "                macro_f1,"
            },
            {
              "line": 341,
              "source": "                s_f1,"
            },
            {
              "line": 342,
              "source": "                -nll,"
            },
            {
              "line": 343,
              "source": "            )"
            },
            {
              "line": 344,
              "source": "        case ChoiceSelectionPriority.FAIL_MISS:"
            },
            {
              "line": 345,
              "source": "            binary = pass_fail_metrics(metrics)"
            },
            {
              "line": 346,
              "source": "            if binary.pass_recall < minimum_pass_recall:"
            },
            {
              "line": 347,
              "source": "                raise CheckpointSelectionError("
            },
            {
              "line": 348,
              "source": "                    detail=\"validation Pass recall is below the required floor\""
            },
            {
              "line": 349,
              "source": "                )"
            },
            {
              "line": 350,
              "source": "            return binary.fail_recall, binary.binary_macro_f1, macro_f1, s_f1, -nll"
            },
            {
              "line": 351,
              "source": "        case ChoiceSelectionPriority.RECALL_FIRST:"
            },
            {
              "line": 352,
              "source": "            binary = pass_fail_metrics(metrics)"
            },
            {
              "line": 353,
              "source": "            fail_to_pass_rate = binary.false_negatives / ("
            },
            {
              "line": 354,
              "source": "                binary.true_positives + binary.false_negatives"
            },
            {
              "line": 355,
              "source": "            )"
            },
            {
              "line": 356,
              "source": "            if ("
            },
            {
              "line": 357,
              "source": "                binary.pass_recall < minimum_pass_recall"
            }
          ]
        }
      ]
    },
    "promotion": {
      "id": "promotion",
      "title": "작업용 test의 최고 모델 승격",
      "claim": "검증 후보 목록과 별도로 작업용 test의 판정 곡 오류 개수로 최고 모델을 갱신합니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/scripts/champion_loop.py",
      "source_path": "scripts/champion_loop.py",
      "sha256": "22ae2920944bfad26825ef2074ee259504c1231b3399f89b04a3c51359634a44",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 122,
          "end_line": 129,
          "lines": [
            {
              "line": 122,
              "source": "def dominates(new: Row, old: Row) -> bool:"
            },
            {
              "line": 123,
              "source": "    \"\"\"Both error counts <= and at least one < (same test set, equal denominators).\"\"\""
            },
            {
              "line": 124,
              "source": "    (new_f2p, new_p2f), (old_f2p, old_p2f) = counts(new), counts(old)"
            },
            {
              "line": 125,
              "source": "    return ("
            },
            {
              "line": 126,
              "source": "        new_f2p <= old_f2p"
            },
            {
              "line": 127,
              "source": "        and new_p2f <= old_p2f"
            },
            {
              "line": 128,
              "source": "        and (new_f2p < old_f2p or new_p2f < old_p2f)"
            },
            {
              "line": 129,
              "source": "    )"
            }
          ]
        },
        {
          "start_line": 132,
          "end_line": 148,
          "lines": [
            {
              "line": 132,
              "source": "def front_champion(front: list[Row], key: str) -> Row:"
            },
            {
              "line": 133,
              "source": "    \"\"\"The front member with the fewest Fail->Pass, then Pass->Fail, then rejections."
            },
            {
              "line": 134,
              "source": ""
            },
            {
              "line": 135,
              "source": "    Dominance alone leaves the champion to evaluation order (the first point of the"
            },
            {
              "line": 136,
              "source": "    front is never dominated by its neighbours), so under band decisions the"
            },
            {
              "line": 137,
              "source": "    champion is this lexicographic best of the front, the same order the reject"
            },
            {
              "line": 138,
              "source": "    band itself is chosen by. Ties fall to the earliest evaluation."
            },
            {
              "line": 139,
              "source": "    \"\"\""
            },
            {
              "line": 140,
              "source": "    return min("
            },
            {
              "line": 141,
              "source": "        front,"
            },
            {
              "line": 142,
              "source": "        key=lambda r: ("
            },
            {
              "line": 143,
              "source": "            r[key][\"fail_to_pass_count\"],"
            },
            {
              "line": 144,
              "source": "            r[key][\"pass_to_fail_count\"],"
            },
            {
              "line": 145,
              "source": "            r[key].get(\"rejected_total\", 0),"
            },
            {
              "line": 146,
              "source": "            r[\"finished\"],"
            },
            {
              "line": 147,
              "source": "        ),"
            },
            {
              "line": 148,
              "source": "    )"
            }
          ]
        },
        {
          "start_line": 528,
          "end_line": 549,
          "lines": [
            {
              "line": 528,
              "source": "    def apply_eval(self, row: Row) -> None:"
            },
            {
              "line": 529,
              "source": "        \"\"\"Track evaluated checkpoints, the champion and the Pareto set.\"\"\""
            },
            {
              "line": 530,
              "source": "        self.evaluated.add((row[\"run_id\"], row[\"checkpoint_name\"], row[\"role\"]))"
            },
            {
              "line": 531,
              "source": "        if ("
            },
            {
              "line": 532,
              "source": "            row[\"role\"] != \"test\""
            },
            {
              "line": 533,
              "source": "            or row[\"status\"] != \"ok\""
            },
            {
              "line": 534,
              "source": "            or row[\"generation\"] != self.generation"
            },
            {
              "line": 535,
              "source": "        ):"
            },
            {
              "line": 536,
              "source": "            return"
            },
            {
              "line": 537,
              "source": "        key = self.compare_key"
            },
            {
              "line": 538,
              "source": "        if row.get(key) is None:  # evaluated before the band existed: not comparable"
            },
            {
              "line": 539,
              "source": "            return"
            },
            {
              "line": 540,
              "source": "        self.evals.append(row)"
            },
            {
              "line": 541,
              "source": "        self.pareto = ("
            },
            {
              "line": 542,
              "source": "            incremental_pareto_front(self.pareto, row, key)"
            },
            {
              "line": 543,
              "source": "            if self.incremental_pareto"
            },
            {
              "line": 544,
              "source": "            else pareto_front(self.evals, key)"
            },
            {
              "line": 545,
              "source": "        )"
            },
            {
              "line": 546,
              "source": "        if self.band_decisions:"
            },
            {
              "line": 547,
              "source": "            self.champion = front_champion(self.pareto, key)"
            },
            {
              "line": 548,
              "source": "        elif self.champion is None or dominates(row[key], self.champion[key]):"
            },
            {
              "line": 549,
              "source": "            self.champion = row"
            }
          ]
        }
      ]
    },
    "serving_decision": {
      "id": "serving_decision",
      "title": "실제 서빙 판정 규칙",
      "claim": "상한 이상 Pass, 하한 이하 Fail, 그 사이 거부이며 Pass일 때 조건부 점수로 A/S를 정합니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/src/raina_laya/features/serving/domain/decision.py",
      "source_path": "src/raina_laya/features/serving/domain/decision.py",
      "sha256": "347bd6930d8f80488d6169ce99575d2fdd83376cda19545471b52a94ff9d21d2",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 7,
          "end_line": 29,
          "lines": [
            {
              "line": 7,
              "source": "def decide("
            },
            {
              "line": 8,
              "source": "    margin: float,"
            },
            {
              "line": 9,
              "source": "    conditional: float,"
            },
            {
              "line": 10,
              "source": "    band: RejectBand | None,"
            },
            {
              "line": 11,
              "source": ") -> tuple[Decision, Grade | None]:"
            },
            {
              "line": 12,
              "source": "    \"\"\"Decide one song from its mean gate margin and mean S-given-Pass logit."
            },
            {
              "line": 13,
              "source": ""
            },
            {
              "line": 14,
              "source": "    With a band, `margin >= hi` is Pass, `margin <= lo` is Fail and anything strictly"
            },
            {
              "line": 15,
              "source": "    between is rejected, as in the research evaluation. Without a band the boundary is"
            },
            {
              "line": 16,
              "source": "    zero and a tie stays Fail. A Pass is S when the conditional logit is above zero and"
            },
            {
              "line": 17,
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              "line": 28,
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            "updated_kst": "2026-10-08 00:18:14 KST"
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    },
    "selection_record": {
      "title": "14세대 선택·승격 차이 사례",
      "claim": "검증 단일 선택은 epoch 20이며, 실제 승격은 검증 후보 목록의 epoch 50입니다.",
      "path": "artifacts/training-runs/champ-g014-r0005-dst_a05/selection.json",
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        "validation_selected_whole": {
          "fail_to_pass_count": 37,
          "fail_count": 2007,
          "pass_to_fail_count": 584,
          "pass_count": 1012,
          "pass_correct_count": 428
        },
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          50
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        ]
      },
      "captured_kst": "2026-10-08 00:18:26 KST"
    },
    "score_bias": {
      "id": "score_bias",
      "title": "공유 계산부와 출력별 편향",
      "claim": "시간 판단부가 낸 세 점수에 학습되는 출력별 편향 3개를 더합니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/src/raina_laya/features/grade_model/domain/v4_model.py",
      "source_path": "src/raina_laya/features/grade_model/domain/v4_model.py",
      "sha256": "42fe06b4023a0b575e8d95cbc1a293d8307ba6ba0ba1029cdc4440586b279d01",
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      "excerpts": [
        {
          "start_line": 35,
          "end_line": 52,
          "lines": [
            {
              "line": 35,
              "source": "        self.temporal_head = TemporalGradeHead("
            },
            {
              "line": 36,
              "source": "            model_width,"
            },
            {
              "line": 37,
              "source": "            transformer_dropout=transformer_dropout,"
            },
            {
              "line": 38,
              "source": "            transformer_depth=transformer_depth,"
            },
            {
              "line": 39,
              "source": "            use_time_encoding=use_time_encoding,"
            },
            {
              "line": 40,
              "source": "            separate_conditional_scorer=separate_conditional_scorer,"
            },
            {
              "line": 41,
              "source": "            detach_conditional_features=detach_conditional_features,"
            },
            {
              "line": 42,
              "source": "            local_temporal_residual=local_temporal_residual,"
            },
            {
              "line": 43,
              "source": "            magnitude_normalizer=magnitude_normalizer,"
            },
            {
              "line": 44,
              "source": "            feature_width=feature_width,"
            },
            {
              "line": 45,
              "source": "        )"
            },
            {
              "line": 46,
              "source": "        self.class_bias = nn.Parameter(torch.tensor(biases, dtype=torch.float32))"
            },
            {
              "line": 47,
              "source": "        output = self.temporal_head.scorer[-1]"
            },
            {
              "line": 48,
              "source": "        if not isinstance(output, nn.Linear):"
            },
            {
              "line": 49,
              "source": "            raise TemporalGradeInputError("
            },
            {
              "line": 50,
              "source": "                detail=\"temporal scorer output must be linear\""
            },
            {
              "line": 51,
              "source": "            )"
            },
            {
              "line": 52,
              "source": "        nn.init.normal_(output.weight, mean=0.0, std=0.001)"
            }
          ]
        },
        {
          "start_line": 65,
          "end_line": 76,
          "lines": [
            {
              "line": 65,
              "source": "    def forward("
            },
            {
              "line": 66,
              "source": "        self,"
            },
            {
              "line": 67,
              "source": "        features: torch.Tensor,"
            },
            {
              "line": 68,
              "source": "        center_times: torch.Tensor,"
            },
            {
              "line": 69,
              "source": "        valid_ratios: torch.Tensor,"
            },
            {
              "line": 70,
              "source": "        padding_mask: torch.Tensor,"
            },
            {
              "line": 71,
              "source": "    ) -> torch.Tensor:"
            },
            {
              "line": 72,
              "source": "        \"\"\"Return uncentered logits with the trainable loss-prior bias.\"\"\""
            },
            {
              "line": 73,
              "source": "        return ("
            },
            {
              "line": 74,
              "source": "            self.temporal_head(features, center_times, valid_ratios, padding_mask)"
            },
            {
              "line": 75,
              "source": "            + self.class_bias"
            },
            {
              "line": 76,
              "source": "        )"
            }
          ]
        }
      ]
    },
    "audio_order": {
      "id": "audio_order",
      "title": "원본 문맥 절단과 소리 준비 순서",
      "claim": "원본 샘플 시각으로 문맥을 절단한 뒤 각 문맥의 모노 변환·리샘플링·채움을 수행합니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/src/raina_laya/features/audio_features/application/build_cache.py",
      "source_path": "src/raina_laya/features/audio_features/application/build_cache.py",
      "sha256": "30cda64293a26e0892ea46b8edf6886c0ecee537ed4b0f549a72183bce888c00",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 118,
          "end_line": 134,
          "lines": [
            {
              "line": 118,
              "source": "    for offset in range(0, len(plan.windows), window_batch_size):"
            },
            {
              "line": 119,
              "source": "        windows = plan.windows[offset : offset + window_batch_size]"
            },
            {
              "line": 120,
              "source": "        contexts = tuple("
            },
            {
              "line": 121,
              "source": "            prepare_audio_context("
            },
            {
              "line": 122,
              "source": "                item.waveform["
            },
            {
              "line": 123,
              "source": "                    ...,"
            },
            {
              "line": 124,
              "source": "                    round(window.start * item.sample_rate) : min("
            },
            {
              "line": 125,
              "source": "                        round(window.end * item.sample_rate),"
            },
            {
              "line": 126,
              "source": "                        item.waveform.shape[-1],"
            },
            {
              "line": 127,
              "source": "                    ),"
            },
            {
              "line": 128,
              "source": "                ],"
            },
            {
              "line": 129,
              "source": "                item.sample_rate,"
            },
            {
              "line": 130,
              "source": "                resampler=resampler,"
            },
            {
              "line": 131,
              "source": "            )"
            },
            {
              "line": 132,
              "source": "            for window in windows"
            },
            {
              "line": 133,
              "source": "        )"
            },
            {
              "line": 134,
              "source": "        if window_batch_size == 1:"
            }
          ]
        },
        {
          "start_line": 196,
          "end_line": 213,
          "lines": [
            {
              "line": 196,
              "source": "    \"\"\""
            },
            {
              "line": 197,
              "source": "    duration_seconds = item.waveform.shape[-1] / item.sample_rate"
            },
            {
              "line": 198,
              "source": "    plan = plan_temporal_bins(duration_seconds)"
            },
            {
              "line": 199,
              "source": "    frames_by_window: dict[float, LayerFrames] = {}"
            },
            {
              "line": 200,
              "source": "    frame_counts: list[int] = []"
            },
            {
              "line": 201,
              "source": "    for window in plan.windows:"
            },
            {
              "line": 202,
              "source": "        context = prepare_audio_context("
            },
            {
              "line": 203,
              "source": "            item.waveform["
            },
            {
              "line": 204,
              "source": "                ...,"
            },
            {
              "line": 205,
              "source": "                round(window.start * item.sample_rate) : min("
            },
            {
              "line": 206,
              "source": "                    round(window.end * item.sample_rate),"
            },
            {
              "line": 207,
              "source": "                    item.waveform.shape[-1],"
            },
            {
              "line": 208,
              "source": "                ),"
            },
            {
              "line": 209,
              "source": "            ],"
            },
            {
              "line": 210,
              "source": "            item.sample_rate,"
            },
            {
              "line": 211,
              "source": "            resampler=resampler,"
            },
            {
              "line": 212,
              "source": "        )"
            },
            {
              "line": 213,
              "source": "        frames = extract_inner_model_layer_frames("
            }
          ]
        }
      ]
    },
    "temporal_bins": {
      "id": "temporal_bins",
      "title": "문맥·꼬리·구간 유효 비율",
      "claim": "실제 구간의 valid_ratio는 1.0이며, 0.5초 미만의 양수 꼬리만 앞 구간과 합칩니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/src/raina_laya/features/audio_features/domain/bins.py",
      "source_path": "src/raina_laya/features/audio_features/domain/bins.py",
      "sha256": "3dab9e4ccf9e2d4d8cbf25c121dd7afba5be77f98f4230fd693df4d60af7e25a",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 151,
          "end_line": 209,
          "lines": [
            {
              "line": 151,
              "source": "def _context_windows("
            },
            {
              "line": 152,
              "source": "    duration_seconds: float,"
            },
            {
              "line": 153,
              "source": "    policy: TemporalBinPolicy,"
            },
            {
              "line": 154,
              "source": ") -> tuple[ContextWindow, ...]:"
            },
            {
              "line": 155,
              "source": "    context = policy.context_seconds"
            },
            {
              "line": 156,
              "source": "    if duration_seconds <= context:"
            },
            {
              "line": 157,
              "source": "        return (ContextWindow(start=0.0, end=context),)"
            },
            {
              "line": 158,
              "source": ""
            },
            {
              "line": 159,
              "source": "    full_count = int(duration_seconds // context)"
            },
            {
              "line": 160,
              "source": "    starts = [float(index) * context for index in range(full_count)]"
            },
            {
              "line": 161,
              "source": "    if starts[-1] + context != duration_seconds:"
            },
            {
              "line": 162,
              "source": "        starts.append(duration_seconds - context)"
            },
            {
              "line": 163,
              "source": "    return tuple(ContextWindow(start=start, end=start + context) for start in starts)"
            },
            {
              "line": 164,
              "source": ""
            },
            {
              "line": 165,
              "source": ""
            },
            {
              "line": 166,
              "source": "def _global_intervals("
            },
            {
              "line": 167,
              "source": "    duration_seconds: float,"
            },
            {
              "line": 168,
              "source": "    policy: TemporalBinPolicy,"
            },
            {
              "line": 169,
              "source": ") -> tuple[tuple[float, float], ...]:"
            },
            {
              "line": 170,
              "source": "    width = policy.pool_bin_seconds"
            },
            {
              "line": 171,
              "source": "    full_count = int(duration_seconds // width)"
            },
            {
              "line": 172,
              "source": "    intervals = ["
            },
            {
              "line": 173,
              "source": "        (float(index) * width, float(index + 1) * width) for index in range(full_count)"
            },
            {
              "line": 174,
              "source": "    ]"
            },
            {
              "line": 175,
              "source": "    full_end = float(full_count) * width"
            },
            {
              "line": 176,
              "source": "    tail = duration_seconds - full_end"
            },
            {
              "line": 177,
              "source": "    if tail > 0:"
            },
            {
              "line": 178,
              "source": "        if tail < policy.min_tail_bin_seconds and intervals:"
            },
            {
              "line": 179,
              "source": "            previous_start, _ = intervals[-1]"
            },
            {
              "line": 180,
              "source": "            intervals[-1] = (previous_start, duration_seconds)"
            },
            {
              "line": 181,
              "source": "        else:"
            },
            {
              "line": 182,
              "source": "            intervals.append((full_end, duration_seconds))"
            },
            {
              "line": 183,
              "source": "    return tuple(intervals)"
            },
            {
              "line": 184,
              "source": ""
            },
            {
              "line": 185,
              "source": ""
            },
            {
              "line": 186,
              "source": "def plan_temporal_bins("
            },
            {
              "line": 187,
              "source": "    duration_seconds: float,"
            },
            {
              "line": 188,
              "source": "    policy: TemporalBinPolicy = DEFAULT_POLICY,"
            },
            {
              "line": 189,
              "source": ") -> TemporalPlan:"
            },
            {
              "line": 190,
              "source": "    \"\"\"Plan fixed contexts and assign each global bin to its earliest owner.\"\"\""
            },
            {
              "line": 191,
              "source": "    if not isfinite(duration_seconds) or duration_seconds <= 0:"
            },
            {
              "line": 192,
              "source": "        raise InvalidDurationError(duration_seconds=duration_seconds)"
            },
            {
              "line": 193,
              "source": "    validated_policy = _validated_policy(policy)"
            },
            {
              "line": 194,
              "source": "    windows = _context_windows(duration_seconds, validated_policy)"
            },
            {
              "line": 195,
              "source": "    bins = tuple("
            },
            {
              "line": 196,
              "source": "        GlobalBin("
            },
            {
              "line": 197,
              "source": "            start=start,"
            },
            {
              "line": 198,
              "source": "            end=end,"
            },
            {
              "line": 199,
              "source": "            center=(start + end) / 2,"
            },
            {
              "line": 200,
              "source": "            valid_ratio=1.0,"
            },
            {
              "line": 201,
              "source": "            owner_window_start=next("
            },
            {
              "line": 202,
              "source": "                window.start"
            },
            {
              "line": 203,
              "source": "                for window in windows"
            },
            {
              "line": 204,
              "source": "                if window.start <= start and end <= window.end"
            },
            {
              "line": 205,
              "source": "            ),"
            },
            {
              "line": 206,
              "source": "        )"
            },
            {
              "line": 207,
              "source": "        for start, end in _global_intervals(duration_seconds, validated_policy)"
            },
            {
              "line": 208,
              "source": "    )"
            },
            {
              "line": 209,
              "source": "    return TemporalPlan(windows=windows, bins=bins)"
            }
          ]
        }
      ]
    },
    "pooling_mask": {
      "id": "pooling_mask",
      "title": "프레임 중심과 채움 제외 범위",
      "claim": "반환된 프레임 수로 중심 시각을 만들고 유효 소리 밖의 중심을 평균에서 제외합니다. MuQ 호출에는 채움 attention_mask를 전달하지 않습니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/src/raina_laya/features/audio_features/infrastructure/features.py",
      "source_path": "src/raina_laya/features/audio_features/infrastructure/features.py",
      "sha256": "aec9c954ee2d06e43fb968632b7e1e420fb637c30059504197ec0c4daa6c1c17",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 323,
          "end_line": 340,
          "lines": [
            {
              "line": 323,
              "source": "def _frame_alignment("
            },
            {
              "line": 324,
              "source": "    frame_count: int,"
            },
            {
              "line": 325,
              "source": "    device: torch.device,"
            },
            {
              "line": 326,
              "source": "    context: AudioContext,"
            },
            {
              "line": 327,
              "source": "    window_start: float,"
            },
            {
              "line": 328,
              "source": ") -> tuple[torch.Tensor, torch.Tensor]:"
            },
            {
              "line": 329,
              "source": "    \"\"\"Return (global frame timestamps, valid-frame mask) of one model context.\"\"\""
            },
            {
              "line": 330,
              "source": "    if frame_count <= 0:"
            },
            {
              "line": 331,
              "source": "        raise HiddenStateContractError(reason=\"selected layers contain no frames\")"
            },
            {
              "line": 332,
              "source": "    context_seconds = context.waveform.numel() / context.sample_rate"
            },
            {
              "line": 333,
              "source": "    frame_width = context_seconds / frame_count"
            },
            {
              "line": 334,
              "source": "    local_centers = ("
            },
            {
              "line": 335,
              "source": "        torch.arange(frame_count, dtype=torch.float32, device=device) + 0.5"
            },
            {
              "line": 336,
              "source": "    ) * frame_width"
            },
            {
              "line": 337,
              "source": "    valid_seconds = context.valid_samples / context.sample_rate"
            },
            {
              "line": 338,
              "source": "    valid_frame_mask = local_centers < valid_seconds"
            },
            {
              "line": 339,
              "source": "    frame_timestamps = local_centers + window_start"
            },
            {
              "line": 340,
              "source": "    return frame_timestamps, valid_frame_mask"
            }
          ]
        },
        {
          "start_line": 417,
          "end_line": 438,
          "lines": [
            {
              "line": 417,
              "source": "def extract_inner_model_layer_frames("
            },
            {
              "line": 418,
              "source": "    model: InnerAudioModel,"
            },
            {
              "line": 419,
              "source": "    context: AudioContext,"
            },
            {
              "line": 420,
              "source": "    *,"
            },
            {
              "line": 421,
              "source": "    window_start: float,"
            },
            {
              "line": 422,
              "source": "    expected_width: int = EXPECTED_HIDDEN_WIDTH,"
            },
            {
              "line": 423,
              "source": ") -> LayerFrames:"
            },
            {
              "line": 424,
              "source": "    \"\"\"Run one context once and retain every hidden state with its alignment.\"\"\""
            },
            {
              "line": 425,
              "source": "    with torch.autocast(device_type=context.waveform.device.type, enabled=False):"
            },
            {
              "line": 426,
              "source": "        output = model("
            },
            {
              "line": 427,
              "source": "            context.waveform.unsqueeze(0).to(dtype=torch.float32),"
            },
            {
              "line": 428,
              "source": "            output_hidden_states=True,"
            },
            {
              "line": 429,
              "source": "        )"
            },
            {
              "line": 430,
              "source": "    layers = _all_hidden_states(output.hidden_states, expected_width)"
            },
            {
              "line": 431,
              "source": "    frame_timestamps, valid_frame_mask = _frame_alignment("
            },
            {
              "line": 432,
              "source": "        layers[0].shape[1], layers[0].device, context, window_start"
            },
            {
              "line": 433,
              "source": "    )"
            },
            {
              "line": 434,
              "source": "    return LayerFrames("
            },
            {
              "line": 435,
              "source": "        layers=layers,"
            },
            {
              "line": 436,
              "source": "        frame_timestamps=frame_timestamps,"
            },
            {
              "line": 437,
              "source": "        valid_frame_mask=valid_frame_mask,"
            },
            {
              "line": 438,
              "source": "    )"
            }
          ]
        },
        {
          "start_line": 464,
          "end_line": 479,
          "lines": [
            {
              "line": 464,
              "source": "    pooled: list[torch.Tensor] = []"
            },
            {
              "line": 465,
              "source": "    for interval in bins:"
            },
            {
              "line": 466,
              "source": "        selected = ("
            },
            {
              "line": 467,
              "source": "            frames.valid_frame_mask"
            },
            {
              "line": 468,
              "source": "            & (frames.frame_timestamps >= interval.start)"
            },
            {
              "line": 469,
              "source": "            & (frames.frame_timestamps < interval.end)"
            },
            {
              "line": 470,
              "source": "        )"
            },
            {
              "line": 471,
              "source": "        rows = selected.nonzero().squeeze(1)"
            },
            {
              "line": 472,
              "source": "        if rows.numel() == 0:"
            },
            {
              "line": 473,
              "source": "            raise EmptyPoolingBinError(start=interval.start, end=interval.end)"
            },
            {
              "line": 474,
              "source": "        pooled.append("
            },
            {
              "line": 475,
              "source": "            torch.stack("
            },
            {
              "line": 476,
              "source": "                [layer[0].index_select(0, rows).mean(dim=0) for layer in frames.layers],"
            },
            {
              "line": 477,
              "source": "            ),"
            },
            {
              "line": 478,
              "source": "        )"
            },
            {
              "line": 479,
              "source": "    return torch.stack(pooled).to(dtype=torch.float32)"
            }
          ]
        }
      ]
    },
    "equal_band_counts": {
      "id": "equal_band_counts",
      "title": "같은 거부 경계의 평가 불일치",
      "claim": "lo==hi의 정확한 동률은 서빙과 평가가 다르게 처리하며, 구간 선택에서는 중복 집계될 수 있습니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/src/raina_laya/features/grade_model/application/v4_evaluate.py",
      "source_path": "src/raina_laya/features/grade_model/application/v4_evaluate.py",
      "sha256": "cc37695f40e7a1d060788978575bc9020ec88e06ecbc03f4bd943ec8672c4e8c",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 204,
          "end_line": 227,
          "lines": [
            {
              "line": 204,
              "source": "def reject_band_counts("
            },
            {
              "line": 205,
              "source": "    margins: Sequence[float], targets: Sequence[int], band: RejectBand"
            },
            {
              "line": 206,
              "source": ") -> dict[str, float]:"
            },
            {
              "line": 207,
              "source": "    \"\"\"Decided-song error counts once songs with lo < margin < hi are rejected.\"\"\""
            },
            {
              "line": 208,
              "source": "    m = np.asarray(margins, dtype=np.float64)"
            },
            {
              "line": 209,
              "source": "    is_pass = np.asarray(targets) != 0"
            },
            {
              "line": 210,
              "source": "    rejected = (m > band.lo) & (m < band.hi)"
            },
            {
              "line": 211,
              "source": "    f2p = int((~is_pass & (m >= band.hi)).sum())"
            },
            {
              "line": 212,
              "source": "    fail_count = int((~is_pass & ~rejected).sum())"
            },
            {
              "line": 213,
              "source": "    p2f = int((is_pass & (m <= band.lo)).sum())"
            },
            {
              "line": 214,
              "source": "    pass_count = int((is_pass & ~rejected).sum())"
            },
            {
              "line": 215,
              "source": "    rejected_pass = int((is_pass & rejected).sum())"
            },
            {
              "line": 216,
              "source": "    return {"
            },
            {
              "line": 217,
              "source": "        \"fail_to_pass_count\": f2p,"
            },
            {
              "line": 218,
              "source": "        \"fail_count\": fail_count,"
            },
            {
              "line": 219,
              "source": "        \"fail_to_pass_rate\": f2p / fail_count if fail_count else 0.0,"
            },
            {
              "line": 220,
              "source": "        \"pass_to_fail_count\": p2f,"
            },
            {
              "line": 221,
              "source": "        \"pass_correct_count\": pass_count - p2f,"
            },
            {
              "line": 222,
              "source": "        \"pass_count\": pass_count,"
            },
            {
              "line": 223,
              "source": "        \"pass_to_fail_rate\": p2f / pass_count if pass_count else 0.0,"
            },
            {
              "line": 224,
              "source": "        \"rejected_total\": int(rejected.sum()),"
            },
            {
              "line": 225,
              "source": "        \"rejected_pass_count\": rejected_pass,"
            },
            {
              "line": 226,
              "source": "        \"rejected_pass_rate\": rejected_pass / max(int(is_pass.sum()), 1),"
            },
            {
              "line": 227,
              "source": "    }"
            }
          ]
        },
        {
          "start_line": 261,
          "end_line": 281,
          "lines": [
            {
              "line": 261,
              "source": "    pass_le_lo = np.searchsorted(pm, lo, side=\"right\")  # Pass decided Fail"
            },
            {
              "line": 262,
              "source": "    pass_ge_hi = pm.size - np.searchsorted(pm, hi, side=\"left\")  # Pass decided Pass"
            },
            {
              "line": 263,
              "source": "    fail_ge_hi = fm.size - np.searchsorted(fm, hi, side=\"left\")  # Fail decided Pass"
            },
            {
              "line": 264,
              "source": "    fail_le_lo = np.searchsorted(fm, lo, side=\"right\")"
            },
            {
              "line": 265,
              "source": "    decided_pass = pass_le_lo + pass_ge_hi"
            },
            {
              "line": 266,
              "source": "    rejected = (pm.size - decided_pass) + (fm.size - fail_le_lo - fail_ge_hi)"
            },
            {
              "line": 267,
              "source": "    p2f_rate = np.divide("
            },
            {
              "line": 268,
              "source": "        pass_le_lo,"
            },
            {
              "line": 269,
              "source": "        decided_pass,"
            },
            {
              "line": 270,
              "source": "        out=np.ones(decided_pass.shape),"
            },
            {
              "line": 271,
              "source": "        where=decided_pass > 0,"
            },
            {
              "line": 272,
              "source": "    )"
            },
            {
              "line": 273,
              "source": "    feasible = ("
            },
            {
              "line": 274,
              "source": "        (lo <= hi)"
            },
            {
              "line": 275,
              "source": "        & (pm.size - decided_pass <= max_pass_loss * pm.size + 1e-9)"
            },
            {
              "line": 276,
              "source": "        & (p2f_rate <= max_p2f_rate + 1e-12)"
            },
            {
              "line": 277,
              "source": "    )"
            },
            {
              "line": 278,
              "source": "    if not feasible.any():"
            },
            {
              "line": 279,
              "source": "        return RejectBand(lo=0.0, hi=0.0)"
            },
            {
              "line": 280,
              "source": "    big = 1 << 40"
            },
            {
              "line": 281,
              "source": "    keys = ["
            }
          ]
        }
      ]
    },
    "band_contract": {
      "id": "band_contract",
      "title": "패키지 거부 경계 계약",
      "claim": "패키지 읽기 계약은 lo>hi만 거부하므로 lo==hi가 허용됩니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/src/raina_laya/features/serving/infrastructure/package.py",
      "source_path": "src/raina_laya/features/serving/infrastructure/package.py",
      "sha256": "ce1f7db6f88a2c48aca69c44945f7630c5468c8b76bfce3ea516d02fd5420172",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 45,
          "end_line": 65,
          "lines": [
            {
              "line": 45,
              "source": "    if ("
            },
            {
              "line": 46,
              "source": "        isinstance(value, bool)"
            },
            {
              "line": 47,
              "source": "        or not isinstance(value, (int, float))"
            },
            {
              "line": 48,
              "source": "        or not math.isfinite(value)"
            },
            {
              "line": 49,
              "source": "    ):"
            },
            {
              "line": 50,
              "source": "        raise BackendLoadError(detail=\"reject_band lo and hi must be finite numbers\")"
            },
            {
              "line": 51,
              "source": "    return float(value)"
            },
            {
              "line": 52,
              "source": ""
            },
            {
              "line": 53,
              "source": ""
            },
            {
              "line": 54,
              "source": "def _band(value: object) -> RejectBand | None:"
            },
            {
              "line": 55,
              "source": "    \"\"\"Parse the packaged reject band; an absent band decides at margin zero.\"\"\""
            },
            {
              "line": 56,
              "source": "    if value is None:"
            },
            {
              "line": 57,
              "source": "        return None"
            },
            {
              "line": 58,
              "source": "    if not isinstance(value, dict):"
            },
            {
              "line": 59,
              "source": "        raise BackendLoadError(detail=\"reject_band must be a mapping\")"
            },
            {
              "line": 60,
              "source": "    band = RejectBand(lo=_bound(value.get(\"lo\")), hi=_bound(value.get(\"hi\")))"
            },
            {
              "line": 61,
              "source": "    if band.lo > band.hi:"
            },
            {
              "line": 62,
              "source": "        raise BackendLoadError(detail=\"reject_band needs lo <= hi\")"
            },
            {
              "line": 63,
              "source": "    return band"
            },
            {
              "line": 64,
              "source": ""
            },
            {
              "line": 65,
              "source": ""
            }
          ]
        }
      ]
    },
    "training_objective": {
      "id": "training_objective",
      "title": "가중 손실과 실제 곡 수 정규화",
      "claim": "외부 클래스 가중치는 곡별 손실 전체에 곱하며, 누적 기울기는 실제 곡 수로 나눕니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/src/raina_laya/features/grade_model/application/v4_train.py",
      "source_path": "src/raina_laya/features/grade_model/application/v4_train.py",
      "sha256": "cd8a02944c48ba5e3a481d30cdaf7e592588c2bf122ae4103107f99176d847b0",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
          "start_line": 98,
          "end_line": 159,
          "lines": [
            {
              "line": 98,
              "source": "def _backward("
            },
            {
              "line": 99,
              "source": "    request: ChoiceTrainRequest,"
            },
            {
              "line": 100,
              "source": "    indices: list[int],"
            },
            {
              "line": 101,
              "source": "    state: _EpochState,"
            },
            {
              "line": 102,
              "source": "    *,"
            },
            {
              "line": 103,
              "source": "    active: bool,"
            },
            {
              "line": 104,
              "source": "    bank: SongBank | None = None,"
            },
            {
              "line": 105,
              "source": ") -> int:"
            },
            {
              "line": 106,
              "source": "    \"\"\"Backpropagate one real or zero-weight synchronization microbatch.\"\"\""
            },
            {
              "line": 107,
              "source": "    validate_choice_config(request.config)"
            },
            {
              "line": 108,
              "source": "    if request.config.stochastic_views != 1 and request.gate_teacher is not None:"
            },
            {
              "line": 109,
              "source": "        raise GradeLossInputError(detail=\"two views reject gate distillation\")"
            },
            {
              "line": 110,
              "source": "    if bank is None:"
            },
            {
              "line": 111,
              "source": "        bank = SongBank(request.songs, request.device)"
            },
            {
              "line": 112,
              "source": "    features, times, ratios, mask, targets = bank.batch(indices)"
            },
            {
              "line": 113,
              "source": "    if request.config.stochastic_views != 1:"
            },
            {
              "line": 114,
              "source": "        features = torch.cat((features, features), dim=0)"
            },
            {
              "line": 115,
              "source": "        times = torch.cat((times, times), dim=0)"
            },
            {
              "line": 116,
              "source": "        ratios = torch.cat((ratios, ratios), dim=0)"
            },
            {
              "line": 117,
              "source": "        mask = torch.cat((mask, mask), dim=0)"
            },
            {
              "line": 118,
              "source": "    with torch.autocast("
            },
            {
              "line": 119,
              "source": "        request.device.type,"
            },
            {
              "line": 120,
              "source": "        dtype=torch.bfloat16,"
            },
            {
              "line": 121,
              "source": "        enabled=request.device.type == \"cuda\","
            },
            {
              "line": 122,
              "source": "    ):"
            },
            {
              "line": 123,
              "source": "        logits = request.head(features, times, ratios, mask)"
            },
            {
              "line": 124,
              "source": "    logits = logits.float()"
            },
            {
              "line": 125,
              "source": "    gate_target = None"
            },
            {
              "line": 126,
              "source": "    if request.gate_teacher is not None and active:"
            },
            {
              "line": 127,
              "source": "        with ("
            },
            {
              "line": 128,
              "source": "            torch.no_grad(),"
            },
            {
              "line": 129,
              "source": "            torch.autocast("
            },
            {
              "line": 130,
              "source": "                request.device.type,"
            },
            {
              "line": 131,
              "source": "                dtype=torch.bfloat16,"
            },
            {
              "line": 132,
              "source": "                enabled=request.device.type == \"cuda\","
            },
            {
              "line": 133,
              "source": "            ),"
            },
            {
              "line": 134,
              "source": "        ):"
            },
            {
              "line": 135,
              "source": "            first, second = request.gate_teacher.heads"
            },
            {
              "line": 136,
              "source": "            first_logits = first(features, times, ratios, mask)"
            },
            {
              "line": 137,
              "source": "            second_logits = second(features, times, ratios, mask)"
            },
            {
              "line": 138,
              "source": "        gate_target = GateTarget("
            },
            {
              "line": 139,
              "source": "            mean_gate_probability(first_logits, second_logits),"
            },
            {
              "line": 140,
              "source": "            request.gate_teacher.weight,"
            },
            {
              "line": 141,
              "source": "        )"
            },
            {
              "line": 142,
              "source": "    if request.gate_soft_targets is not None:"
            },
            {
              "line": 143,
              "source": "        soft = [request.gate_soft_targets[index] for index in indices]"
            },
            {
              "line": 144,
              "source": "        state = replace("
            },
            {
              "line": 145,
              "source": "            state,"
            },
            {
              "line": 146,
              "source": "            gate_soft_target=torch.tensor("
            },
            {
              "line": 147,
              "source": "                soft, dtype=logits.dtype, device=logits.device"
            },
            {
              "line": 148,
              "source": "            ),"
            },
            {
              "line": 149,
              "source": "        )"
            },
            {
              "line": 150,
              "source": "    per_song = choice_objective("
            },
            {
              "line": 151,
              "source": "        logits,"
            },
            {
              "line": 152,
              "source": "        targets,"
            },
            {
              "line": 153,
              "source": "        request.config,"
            },
            {
              "line": 154,
              "source": "        replace(state, gate_target=gate_target) if gate_target is not None else state,"
            },
            {
              "line": 155,
              "source": "        active=active,"
            },
            {
              "line": 156,
              "source": "    )"
            },
            {
              "line": 157,
              "source": "    numerator = (state.weights[targets] * per_song).sum()"
            },
            {
              "line": 158,
              "source": "    (numerator if active else numerator * 0.0).backward()"
            },
            {
              "line": 159,
              "source": "    return len(indices) if active else 0"
            }
          ]
        },
        {
          "start_line": 162,
          "end_line": 187,
          "lines": [
            {
              "line": 162,
              "source": "def _update("
            },
            {
              "line": 163,
              "source": "    request: ChoiceTrainRequest,"
            },
            {
              "line": 164,
              "source": "    optimizer: torch.optim.AdamW,"
            },
            {
              "line": 165,
              "source": "    count: int,"
            },
            {
              "line": 166,
              "source": "    step: int,"
            },
            {
              "line": 167,
              "source": "    total_steps: int,"
            },
            {
              "line": 168,
              "source": ") -> bool:"
            },
            {
              "line": 169,
              "source": "    \"\"\"Normalize by global real song count, then clip one global gradient norm."
            },
            {
              "line": 170,
              "source": ""
            },
            {
              "line": 171,
              "source": "    Detached conditional features block direct encoder gradients, but conditional"
            },
            {
              "line": 172,
              "source": "    gradient magnitudes can still rescale gate gradients through global clipping."
            },
            {
              "line": 173,
              "source": "    \"\"\""
            },
            {
              "line": 174,
              "source": "    observed = torch.tensor(float(count), device=request.device)"
            },
            {
              "line": 175,
              "source": "    if request.world_size > 1:"
            },
            {
              "line": 176,
              "source": "        dist.all_reduce(observed)"
            },
            {
              "line": 177,
              "source": "    denominator = observed.item()"
            },
            {
              "line": 178,
              "source": "    for parameter in request.head.parameters():"
            },
            {
              "line": 179,
              "source": "        if parameter.grad is not None:"
            },
            {
              "line": 180,
              "source": "            parameter.grad.mul_(request.world_size / denominator)"
            },
            {
              "line": 181,
              "source": "    rate = _learning_rate(step, total_steps, request.config)"
            },
            {
              "line": 182,
              "source": "    for group in optimizer.param_groups:"
            },
            {
              "line": 183,
              "source": "        group[\"lr\"] = rate"
            },
            {
              "line": 184,
              "source": "    norm = nn.utils.clip_grad_norm_(request.head.parameters(), request.config.grad_clip)"
            },
            {
              "line": 185,
              "source": "    optimizer.step()"
            },
            {
              "line": 186,
              "source": "    optimizer.zero_grad(set_to_none=True)"
            },
            {
              "line": 187,
              "source": "    return bool(norm > request.config.grad_clip)"
            }
          ]
        },
        {
          "start_line": 71,
          "end_line": 95,
          "lines": [
            {
              "line": 71,
              "source": "def _optimizer(head: nn.Module, config: ChoiceTrainConfig) -> torch.optim.AdamW:"
            },
            {
              "line": 72,
              "source": "    \"\"\"Keep the prior bias outside AdamW weight decay.\"\"\""
            },
            {
              "line": 73,
              "source": "    return torch.optim.AdamW("
            },
            {
              "line": 74,
              "source": "        ["
            },
            {
              "line": 75,
              "source": "            {"
            },
            {
              "line": 76,
              "source": "                \"params\": ["
            },
            {
              "line": 77,
              "source": "                    parameter"
            },
            {
              "line": 78,
              "source": "                    for name, parameter in head.named_parameters()"
            },
            {
              "line": 79,
              "source": "                    if name not in {\"module.class_bias\", \"class_bias\"}"
            },
            {
              "line": 80,
              "source": "                ],"
            },
            {
              "line": 81,
              "source": "                \"weight_decay\": config.weight_decay,"
            },
            {
              "line": 82,
              "source": "            },"
            },
            {
              "line": 83,
              "source": "            {"
            },
            {
              "line": 84,
              "source": "                \"params\": ["
            },
            {
              "line": 85,
              "source": "                    parameter"
            },
            {
              "line": 86,
              "source": "                    for name, parameter in head.named_parameters()"
            },
            {
              "line": 87,
              "source": "                    if name in {\"module.class_bias\", \"class_bias\"}"
            },
            {
              "line": 88,
              "source": "                ],"
            },
            {
              "line": 89,
              "source": "                \"weight_decay\": 0.0,"
            },
            {
              "line": 90,
              "source": "            },"
            },
            {
              "line": 91,
              "source": "        ],"
            },
            {
              "line": 92,
              "source": "        lr=config.learning_rate,"
            },
            {
              "line": 93,
              "source": "        betas=(0.9, config.adam_beta2),"
            },
            {
              "line": 94,
              "source": "        fused=any(parameter.is_cuda for parameter in head.parameters()),"
            },
            {
              "line": 95,
              "source": "    )"
            }
          ]
        }
      ]
    },
    "ensemble_inference": {
      "id": "ensemble_inference",
      "title": "앙상블 추론과 API 출력",
      "claim": "관문 차이와 조건부 로짓을 평균하며, v5 API의 logits 필드는 결합 로그 확률을 반환합니다.",
      "path": "_docs/20261007_모델_설명/audit_20261008/frozen_sources/src/raina_laya/features/serving/application/runtime.py",
      "source_path": "src/raina_laya/features/serving/application/runtime.py",
      "sha256": "bd9ccb1e017fec59b69242b71c45bd667874977b44b855e6904dbb5863d0decc",
      "captured_kst": "2026-10-08 11:51:24 KST",
      "excerpts": [
        {
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        "source_checkpoint_sha256_matches_provenance": true,
        "source_checkpoint_sha256_matches_test": true,
        "source_checkpoint_sha256_matches_reserve": true,
        "source_config_sha256_matches_provenance": true,
        "source_test_eval_sha256_matches_provenance": true,
        "packaged_test_eval_sha256_matches_provenance": true,
        "split_sha256_matches_provenance": true,
        "validation_score_sha256_matches_band_source": true,
        "test_reserve_band_identical": true,
        "test_reserve_split_digest_identical": true,
        "reserve_check_run_id_matches": true,
        "reserve_check_epoch_matches": true,
        "reserve_check_test_decided_matches_report": true,
        "reserve_check_reserve_decided_matches_report": true,
        "teacher_source_sha256_matches": true,
        "teacher_packaged_sha256_matches": true,
        "local_packaged_checkpoint_sha256_matches": true,
        "promoted_epoch_in_validation_shortlist": true,
        "validation_selected_epoch_differs_from_promoted_epoch": true,
        "validation_pass_loss_within_limit": true,
        "validation_decided_pass_to_fail_rate_within_zero_boundary_rate": true,
        "package_finished_exit_code_zero": true,
        "test_whole_fail_to_pass_rate_recomputed": true,
        "test_whole_pass_to_fail_rate_recomputed": true,
        "test_decided_fail_to_pass_rate_recomputed": true,
        "test_decided_pass_to_fail_rate_recomputed": true,
        "test_decided_rejected_pass_rate_recomputed": true,
        "reserve_whole_fail_to_pass_rate_recomputed": true,
        "reserve_whole_pass_to_fail_rate_recomputed": true,
        "reserve_decided_fail_to_pass_rate_recomputed": true,
        "reserve_decided_pass_to_fail_rate_recomputed": true,
        "reserve_decided_rejected_pass_rate_recomputed": true
      }
    }
  }
}