Remove legacy Sleep Assessment case paths after numeric ordering

sleep_assessment/cases/case1/case1_01.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "ISRUC_01.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "ISRUC"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/ISRUC_01.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 5.5,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_02.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "ISRUC_02.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "ISRUC"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/ISRUC_02.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 55.0,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_03.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "ISRUC_03.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "ISRUC"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/ISRUC_03.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 3.5,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_04.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "ISRUC_04.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "ISRUC"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/ISRUC_04.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 1.0,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_05.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "ISRUC_05.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "ISRUC"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/ISRUC_05.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 75.0,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_06.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "HMC_01.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "HMC"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/HMC_01.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 4.0,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_07.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "HMC_02.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "HMC"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/HMC_02.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 3.0,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_08.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "HMC_03.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "HMC"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/HMC_03.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 1.5,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_09.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "HMC_04.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "HMC"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/HMC_04.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 3.0,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_10.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "HMC_05.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "HMC"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/HMC_05.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 22.0,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_11.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "SHHS1_01.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "SHHS1"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/SHHS1_01.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 14.0,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_12.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "SHHS1_02.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "SHHS1"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/SHHS1_02.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 0.0,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_13.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "SHHS1_03.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "SHHS1"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/SHHS1_03.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 83.5,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_14.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "SHHS1_04.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "SHHS1"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/SHHS1_04.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 41.0,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_15.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "SHHS1_05.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "SHHS1"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/SHHS1_05.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 10.0,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_16.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "MASSSS3_01.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "MASSSS3"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/MASSSS3_01.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 3.5,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_17.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "MASSSS3_02.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "MASSSS3"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/MASSSS3_02.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 21.0,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_18.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "MASSSS3_03.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "MASSSS3"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/MASSSS3_03.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 7.0,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_19.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "MASSSS3_04.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "MASSSS3"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/MASSSS3_04.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 2.0,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_20.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "MASSSS3_05.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "MASSSS3"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/MASSSS3_05.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 7.0,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_21.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "Physionet2018_01.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "Physionet2018"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/Physionet2018_01.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 4.0,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_22.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "Physionet2018_02.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "Physionet2018"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/Physionet2018_02.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 10.5,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_23.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "Physionet2018_03.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "Physionet2018"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/Physionet2018_03.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 4.0,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_24.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "Physionet2018_04.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "Physionet2018"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/Physionet2018_04.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 0.5,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case1/case1_25.json

@@ -1,27 +0,0 @@
-{
-    "meta_info": {
-        "case_id": "Physionet2018_05.edf",
-        "bench_subset": "NeuroBench-Sleep",
-        "difficult": 1,
-        "original_dataset": "Physionet2018"
-    },
-    "agent_input": {
-        "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.",
-        "label_path": "data/sleep/Physionet2018_05.npy"
-    },
-    "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 the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\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 key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": <float>}",
-        "metrics": [
-            {
-                "metric_id": "sleep_onset_latency_accuracy",
-                "type": "numeric_check",
-                "target_key": "sleep_onset_latency_min",
-                "weight": 100,
-                "params": {
-                    "gt_value": 1.0,
-                    "tolerance": 2.0
-                }
-            }
-        ]
-    }
-}

sleep_assessment/cases/case10/case10_01.json

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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

@@ -1,37 +0,0 @@
-{
-    "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
-                }
-            }
-        ]
-    }
-}