Restore naturally ordered Sleep Assessment case paths

sleep_assessment/cases/case10/case10_01.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "ISRUC_01.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "ISRUC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/ISRUC_01.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 14,
+                    "tolerance": 0.7000000000000001
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 8.142857142857142,
+                    "tolerance": 0.40714285714285714
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_02.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "ISRUC_02.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "ISRUC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/ISRUC_02.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 7,
+                    "tolerance": 0.35000000000000003
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 10.928571428571429,
+                    "tolerance": 0.5464285714285715
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_03.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "ISRUC_03.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "ISRUC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/ISRUC_03.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 5,
+                    "tolerance": 0.25
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 17.2,
+                    "tolerance": 0.86
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_04.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "ISRUC_04.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "ISRUC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/ISRUC_04.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 8,
+                    "tolerance": 0.4
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 13.375,
+                    "tolerance": 0.6687500000000001
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_05.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "ISRUC_05.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "ISRUC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/ISRUC_05.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 5,
+                    "tolerance": 0.25
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 16.3,
+                    "tolerance": 0.8150000000000001
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_06.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "HMC_01.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "HMC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/HMC_01.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 6,
+                    "tolerance": 0.30000000000000004
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 1.75,
+                    "tolerance": 0.08750000000000001
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_07.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "HMC_02.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "HMC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/HMC_02.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 14,
+                    "tolerance": 0.7000000000000001
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 7.821428571428571,
+                    "tolerance": 0.39107142857142857
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_08.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "HMC_03.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "HMC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/HMC_03.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 14,
+                    "tolerance": 0.7000000000000001
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 5.321428571428571,
+                    "tolerance": 0.26607142857142857
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_09.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "HMC_04.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "HMC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/HMC_04.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 8,
+                    "tolerance": 0.4
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 3.9375,
+                    "tolerance": 0.19687500000000002
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_10.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "HMC_05.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "HMC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/HMC_05.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 10,
+                    "tolerance": 0.5
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 7.15,
+                    "tolerance": 0.35750000000000004
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_11.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "SHHS1_01.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "SHHS1"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/SHHS1_01.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 15,
+                    "tolerance": 0.75
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 4.366666666666666,
+                    "tolerance": 0.21833333333333332
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_12.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "SHHS1_02.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "SHHS1"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/SHHS1_02.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 12,
+                    "tolerance": 0.6000000000000001
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 1.7916666666666667,
+                    "tolerance": 0.08958333333333335
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_13.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "SHHS1_03.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "SHHS1"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/SHHS1_03.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 18,
+                    "tolerance": 0.9
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 8.222222222222221,
+                    "tolerance": 0.4111111111111111
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_14.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "SHHS1_04.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "SHHS1"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/SHHS1_04.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 17,
+                    "tolerance": 0.8500000000000001
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 8.588235294117647,
+                    "tolerance": 0.4294117647058824
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_15.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "SHHS1_05.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "SHHS1"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/SHHS1_05.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 13,
+                    "tolerance": 0.65
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 3.4615384615384617,
+                    "tolerance": 0.1730769230769231
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_16.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "MASSSS3_01.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "MASSSS3"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/MASSSS3_01.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 26,
+                    "tolerance": 1.3
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 2.3846153846153846,
+                    "tolerance": 0.11923076923076924
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_17.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "MASSSS3_02.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "MASSSS3"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/MASSSS3_02.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 19,
+                    "tolerance": 0.9500000000000001
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 4.052631578947368,
+                    "tolerance": 0.2026315789473684
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_18.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "MASSSS3_03.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "MASSSS3"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/MASSSS3_03.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 19,
+                    "tolerance": 0.9500000000000001
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 2.8421052631578947,
+                    "tolerance": 0.14210526315789473
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_19.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "MASSSS3_04.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "MASSSS3"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/MASSSS3_04.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 10,
+                    "tolerance": 0.5
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 2.65,
+                    "tolerance": 0.1325
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_20.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "MASSSS3_05.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "MASSSS3"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/MASSSS3_05.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 6,
+                    "tolerance": 0.30000000000000004
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 15.833333333333334,
+                    "tolerance": 0.7916666666666667
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_21.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "Physionet2018_01.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "Physionet2018"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/Physionet2018_01.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 4,
+                    "tolerance": 0.2
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 8.875,
+                    "tolerance": 0.44375000000000003
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_22.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "Physionet2018_02.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "Physionet2018"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/Physionet2018_02.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 4,
+                    "tolerance": 0.2
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 2.625,
+                    "tolerance": 0.13125
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_23.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "Physionet2018_03.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "Physionet2018"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/Physionet2018_03.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 4,
+                    "tolerance": 0.2
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 6.5,
+                    "tolerance": 0.325
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_24.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "Physionet2018_04.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "Physionet2018"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/Physionet2018_04.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 19,
+                    "tolerance": 0.9500000000000001
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 4.342105263157895,
+                    "tolerance": 0.21710526315789475
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case10/case10_25.json

@@ -0,0 +1,37 @@
+{
+    "meta_info": {
+        "case_id": "Physionet2018_05.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "Physionet2018"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/Physionet2018_05.npy",
+        "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": <integer|null>, \"n3_mean_bout_length_min\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "n3_bout_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_bout_count",
+                "weight": 50,
+                "params": {
+                    "gt_value": 4,
+                    "tolerance": 0.2
+                }
+            },
+            {
+                "metric_id": "n3_mean_bout_length_accuracy",
+                "type": "numeric_check",
+                "target_key": "n3_mean_bout_length_min",
+                "weight": 50,
+                "params": {
+                    "gt_value": 12.0,
+                    "tolerance": 0.6000000000000001
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_01.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "ISRUC_01.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "ISRUC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/ISRUC_01.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 353.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 391.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "high"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_02.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "ISRUC_02.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "ISRUC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/ISRUC_02.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 4.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 32.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "medium"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_03.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "ISRUC_03.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "ISRUC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/ISRUC_03.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 241.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 256.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "medium"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_04.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "ISRUC_04.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "ISRUC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/ISRUC_04.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 256.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 258.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "low"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_05.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "ISRUC_05.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "ISRUC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/ISRUC_05.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 21.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 33.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "medium"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_06.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "HMC_01.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "HMC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/HMC_01.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 154.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 196.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "high"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_07.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "HMC_02.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "HMC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/HMC_02.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 397.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 405.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "medium"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_08.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "HMC_03.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "HMC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/HMC_03.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 148.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 195.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "high"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_09.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "HMC_04.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "HMC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/HMC_04.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 493.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 500.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "medium"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_10.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "HMC_05.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "HMC"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/HMC_05.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 112.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 122.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "medium"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_11.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "SHHS1_01.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "SHHS1"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/SHHS1_01.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 47.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 56.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "medium"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_12.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "SHHS1_02.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "SHHS1"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/SHHS1_02.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 56.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 66.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "medium"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_13.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "SHHS1_03.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "SHHS1"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/SHHS1_03.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 282.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 301.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "medium"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_14.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "SHHS1_04.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "SHHS1"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/SHHS1_04.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 50.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 56.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "medium"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_15.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "SHHS1_05.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "SHHS1"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/SHHS1_05.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 327.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 346.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "medium"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_16.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "MASSSS3_01.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "MASSSS3"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/MASSSS3_01.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 410.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 422.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "medium"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_17.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "MASSSS3_02.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "MASSSS3"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/MASSSS3_02.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 486.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 494.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "medium"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_18.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "MASSSS3_03.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "MASSSS3"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/MASSSS3_03.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 400.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 405.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "low"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_19.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "MASSSS3_04.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "MASSSS3"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/MASSSS3_04.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 174.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 174.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "low"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_20.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "MASSSS3_05.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "MASSSS3"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/MASSSS3_05.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 385.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 390.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "medium"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_21.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "Physionet2018_01.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "Physionet2018"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/Physionet2018_01.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 270.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 353.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "high"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_22.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "Physionet2018_02.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "Physionet2018"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/Physionet2018_02.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 81.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 107.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "medium"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_23.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "Physionet2018_03.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "Physionet2018"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/Physionet2018_03.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 131.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 254.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "high"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_24.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "Physionet2018_04.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "Physionet2018"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/Physionet2018_04.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 265.5,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 281.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "medium"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case11/case11_25.json

@@ -0,0 +1,46 @@
+{
+    "meta_info": {
+        "case_id": "Physionet2018_05.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1.5,
+        "original_dataset": "Physionet2018"
+    },
+    "agent_input": {
+        "label_path": "data/sleep/Physionet2018_05.npy",
+        "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": <float|null>, \"longest_w_end_min\": <float|null>, \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}",
+        "metrics": [
+            {
+                "metric_id": "longest_w_start_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_start_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 204.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "longest_w_end_time_accuracy",
+                "type": "numeric_check",
+                "target_key": "longest_w_end_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 231.0,
+                    "tolerance": 2.0
+                }
+            },
+            {
+                "metric_id": "wake_interruption_impact_accuracy",
+                "type": "categorical_check",
+                "target_key": "wake_interruption_impact",
+                "weight": 40,
+                "params": {
+                    "gt_value": "medium"
+                }
+            }
+        ]
+    }
+}