Sync Sleep Assessment JSON cases from local

sleep_assessment/cases/case1/case1_01.json

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+{
+    "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

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+{
+    "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

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+{
+    "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

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+{
+    "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

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+{
+    "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

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+{
+    "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

@@ -0,0 +1,27 @@
+{
+    "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

@@ -0,0 +1,27 @@
+{
+    "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

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+{
+    "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

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+{
+    "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

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+{
+    "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

@@ -0,0 +1,27 @@
+{
+    "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

@@ -0,0 +1,27 @@
+{
+    "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

@@ -0,0 +1,27 @@
+{
+    "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

@@ -0,0 +1,27 @@
+{
+    "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

@@ -0,0 +1,27 @@
+{
+    "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

@@ -0,0 +1,27 @@
+{
+    "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

@@ -0,0 +1,27 @@
+{
+    "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

@@ -0,0 +1,27 @@
+{
+    "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

@@ -0,0 +1,27 @@
+{
+    "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

@@ -0,0 +1,27 @@
+{
+    "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

@@ -0,0 +1,27 @@
+{
+    "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

@@ -0,0 +1,27 @@
+{
+    "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

@@ -0,0 +1,27 @@
+{
+    "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

@@ -0,0 +1,27 @@
+{
+    "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/case2/case2_01.json

@@ -0,0 +1,44 @@
+{
+    "meta_info": {
+        "case_id": "ISRUC_01.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "ISRUC"
+    },
+    "agent_input": {
+        "data_path": "data/sleep/ISRUC_01.edf",
+        "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\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 or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": <integer|null>, \"eeg_channels\": <array|null>}",
+        "metrics": [
+            {
+                "metric_id": "channel_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "channel_count",
+                "weight": 30,
+                "params": {
+                    "gt_value": 19,
+                    "tolerance": 0.1
+                }
+            },
+            {
+                "metric_id": "eeg_channel_set_match",
+                "type": "set_match_check",
+                "target_key": "eeg_channels",
+                "weight": 70,
+                "params": {
+                    "gt_value": [
+                        "F3-A2",
+                        "C3-A2",
+                        "O1-A2",
+                        "F4-A1",
+                        "C4-A1",
+                        "O2-A1"
+                    ],
+                    "match_mode": "exact"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case2/case2_02.json

@@ -0,0 +1,44 @@
+{
+    "meta_info": {
+        "case_id": "ISRUC_02.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "ISRUC"
+    },
+    "agent_input": {
+        "data_path": "data/sleep/ISRUC_02.edf",
+        "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\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 or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": <integer|null>, \"eeg_channels\": <array|null>}",
+        "metrics": [
+            {
+                "metric_id": "channel_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "channel_count",
+                "weight": 30,
+                "params": {
+                    "gt_value": 19,
+                    "tolerance": 0.1
+                }
+            },
+            {
+                "metric_id": "eeg_channel_set_match",
+                "type": "set_match_check",
+                "target_key": "eeg_channels",
+                "weight": 70,
+                "params": {
+                    "gt_value": [
+                        "F3-A2",
+                        "C3-A2",
+                        "O1-A2",
+                        "F4-A1",
+                        "C4-A1",
+                        "O2-A1"
+                    ],
+                    "match_mode": "exact"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case2/case2_03.json

@@ -0,0 +1,44 @@
+{
+    "meta_info": {
+        "case_id": "ISRUC_03.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "ISRUC"
+    },
+    "agent_input": {
+        "data_path": "data/sleep/ISRUC_03.edf",
+        "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\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 or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": <integer|null>, \"eeg_channels\": <array|null>}",
+        "metrics": [
+            {
+                "metric_id": "channel_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "channel_count",
+                "weight": 30,
+                "params": {
+                    "gt_value": 19,
+                    "tolerance": 0.1
+                }
+            },
+            {
+                "metric_id": "eeg_channel_set_match",
+                "type": "set_match_check",
+                "target_key": "eeg_channels",
+                "weight": 70,
+                "params": {
+                    "gt_value": [
+                        "F3-A2",
+                        "C3-A2",
+                        "O1-A2",
+                        "F4-A1",
+                        "C4-A1",
+                        "O2-A1"
+                    ],
+                    "match_mode": "exact"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case2/case2_04.json

@@ -0,0 +1,42 @@
+{
+    "meta_info": {
+        "case_id": "HMC_01.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "HMC"
+    },
+    "agent_input": {
+        "data_path": "data/sleep/HMC_01.edf",
+        "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\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 or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": <integer|null>, \"eeg_channels\": <array|null>}",
+        "metrics": [
+            {
+                "metric_id": "channel_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "channel_count",
+                "weight": 30,
+                "params": {
+                    "gt_value": 8,
+                    "tolerance": 0.1
+                }
+            },
+            {
+                "metric_id": "eeg_channel_set_match",
+                "type": "set_match_check",
+                "target_key": "eeg_channels",
+                "weight": 70,
+                "params": {
+                    "gt_value": [
+                        "EEG F4-M1",
+                        "EEG C4-M1",
+                        "EEG O2-M1",
+                        "EEG C3-M2"
+                    ],
+                    "match_mode": "exact"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case2/case2_05.json

@@ -0,0 +1,42 @@
+{
+    "meta_info": {
+        "case_id": "HMC_02.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "HMC"
+    },
+    "agent_input": {
+        "data_path": "data/sleep/HMC_02.edf",
+        "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\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 or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": <integer|null>, \"eeg_channels\": <array|null>}",
+        "metrics": [
+            {
+                "metric_id": "channel_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "channel_count",
+                "weight": 30,
+                "params": {
+                    "gt_value": 8,
+                    "tolerance": 0.1
+                }
+            },
+            {
+                "metric_id": "eeg_channel_set_match",
+                "type": "set_match_check",
+                "target_key": "eeg_channels",
+                "weight": 70,
+                "params": {
+                    "gt_value": [
+                        "EEG F4-M1",
+                        "EEG C4-M1",
+                        "EEG O2-M1",
+                        "EEG C3-M2"
+                    ],
+                    "match_mode": "exact"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case2/case2_06.json

@@ -0,0 +1,42 @@
+{
+    "meta_info": {
+        "case_id": "HMC_03.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "HMC"
+    },
+    "agent_input": {
+        "data_path": "data/sleep/HMC_03.edf",
+        "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\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 or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": <integer|null>, \"eeg_channels\": <array|null>}",
+        "metrics": [
+            {
+                "metric_id": "channel_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "channel_count",
+                "weight": 30,
+                "params": {
+                    "gt_value": 8,
+                    "tolerance": 0.1
+                }
+            },
+            {
+                "metric_id": "eeg_channel_set_match",
+                "type": "set_match_check",
+                "target_key": "eeg_channels",
+                "weight": 70,
+                "params": {
+                    "gt_value": [
+                        "EEG F4-M1",
+                        "EEG C4-M1",
+                        "EEG O2-M1",
+                        "EEG C3-M2"
+                    ],
+                    "match_mode": "exact"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case2/case2_07.json

@@ -0,0 +1,40 @@
+{
+    "meta_info": {
+        "case_id": "SHHS1_01.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "SHHS1"
+    },
+    "agent_input": {
+        "data_path": "data/sleep/SHHS1_01.edf",
+        "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\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 or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": <integer|null>, \"eeg_channels\": <array|null>}",
+        "metrics": [
+            {
+                "metric_id": "channel_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "channel_count",
+                "weight": 30,
+                "params": {
+                    "gt_value": 14,
+                    "tolerance": 0.1
+                }
+            },
+            {
+                "metric_id": "eeg_channel_set_match",
+                "type": "set_match_check",
+                "target_key": "eeg_channels",
+                "weight": 70,
+                "params": {
+                    "gt_value": [
+                        "EEG(sec)",
+                        "EEG"
+                    ],
+                    "match_mode": "exact"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case2/case2_08.json

@@ -0,0 +1,40 @@
+{
+    "meta_info": {
+        "case_id": "SHHS1_02.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "SHHS1"
+    },
+    "agent_input": {
+        "data_path": "data/sleep/SHHS1_02.edf",
+        "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\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 or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": <integer|null>, \"eeg_channels\": <array|null>}",
+        "metrics": [
+            {
+                "metric_id": "channel_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "channel_count",
+                "weight": 30,
+                "params": {
+                    "gt_value": 14,
+                    "tolerance": 0.1
+                }
+            },
+            {
+                "metric_id": "eeg_channel_set_match",
+                "type": "set_match_check",
+                "target_key": "eeg_channels",
+                "weight": 70,
+                "params": {
+                    "gt_value": [
+                        "EEG(sec)",
+                        "EEG"
+                    ],
+                    "match_mode": "exact"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case2/case2_09.json

@@ -0,0 +1,40 @@
+{
+    "meta_info": {
+        "case_id": "SHHS1_03.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "SHHS1"
+    },
+    "agent_input": {
+        "data_path": "data/sleep/SHHS1_03.edf",
+        "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\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 or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": <integer|null>, \"eeg_channels\": <array|null>}",
+        "metrics": [
+            {
+                "metric_id": "channel_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "channel_count",
+                "weight": 30,
+                "params": {
+                    "gt_value": 14,
+                    "tolerance": 0.1
+                }
+            },
+            {
+                "metric_id": "eeg_channel_set_match",
+                "type": "set_match_check",
+                "target_key": "eeg_channels",
+                "weight": 70,
+                "params": {
+                    "gt_value": [
+                        "EEG(sec)",
+                        "EEG"
+                    ],
+                    "match_mode": "exact"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case2/case2_10.json

@@ -0,0 +1,58 @@
+{
+    "meta_info": {
+        "case_id": "MASSSS3_01.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "MASSSS3"
+    },
+    "agent_input": {
+        "data_path": "data/sleep/MASSSS3_01.edf",
+        "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\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 or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": <integer|null>, \"eeg_channels\": <array|null>}",
+        "metrics": [
+            {
+                "metric_id": "channel_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "channel_count",
+                "weight": 30,
+                "params": {
+                    "gt_value": 27,
+                    "tolerance": 0.1
+                }
+            },
+            {
+                "metric_id": "eeg_channel_set_match",
+                "type": "set_match_check",
+                "target_key": "eeg_channels",
+                "weight": 70,
+                "params": {
+                    "gt_value": [
+                        "EEG Fp1-LER",
+                        "EEG Fp2-LER",
+                        "EEG F7-LER",
+                        "EEG F8-LER",
+                        "EEG F3-LER",
+                        "EEG F4-LER",
+                        "EEG T3-LER",
+                        "EEG T4-LER",
+                        "EEG C3-LER",
+                        "EEG C4-LER",
+                        "EEG T5-LER",
+                        "EEG T6-LER",
+                        "EEG P3-LER",
+                        "EEG P4-LER",
+                        "EEG O1-LER",
+                        "EEG O2-LER",
+                        "EEG Fz-LER",
+                        "EEG Cz-LER",
+                        "EEG Pz-LER",
+                        "EEG Oz-LER"
+                    ],
+                    "match_mode": "exact"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case2/case2_11.json

@@ -0,0 +1,58 @@
+{
+    "meta_info": {
+        "case_id": "MASSSS3_02.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "MASSSS3"
+    },
+    "agent_input": {
+        "data_path": "data/sleep/MASSSS3_02.edf",
+        "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\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 or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": <integer|null>, \"eeg_channels\": <array|null>}",
+        "metrics": [
+            {
+                "metric_id": "channel_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "channel_count",
+                "weight": 30,
+                "params": {
+                    "gt_value": 27,
+                    "tolerance": 0.1
+                }
+            },
+            {
+                "metric_id": "eeg_channel_set_match",
+                "type": "set_match_check",
+                "target_key": "eeg_channels",
+                "weight": 70,
+                "params": {
+                    "gt_value": [
+                        "EEG Fp1-LER",
+                        "EEG Fp2-LER",
+                        "EEG F7-LER",
+                        "EEG F8-LER",
+                        "EEG F3-LER",
+                        "EEG F4-LER",
+                        "EEG T3-LER",
+                        "EEG T4-LER",
+                        "EEG C3-LER",
+                        "EEG C4-LER",
+                        "EEG T5-LER",
+                        "EEG T6-LER",
+                        "EEG P3-LER",
+                        "EEG P4-LER",
+                        "EEG O1-LER",
+                        "EEG O2-LER",
+                        "EEG Fz-LER",
+                        "EEG Cz-LER",
+                        "EEG Pz-LER",
+                        "EEG Oz-LER"
+                    ],
+                    "match_mode": "exact"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case2/case2_12.json

@@ -0,0 +1,58 @@
+{
+    "meta_info": {
+        "case_id": "MASSSS3_03.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "MASSSS3"
+    },
+    "agent_input": {
+        "data_path": "data/sleep/MASSSS3_03.edf",
+        "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\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 or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": <integer|null>, \"eeg_channels\": <array|null>}",
+        "metrics": [
+            {
+                "metric_id": "channel_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "channel_count",
+                "weight": 30,
+                "params": {
+                    "gt_value": 27,
+                    "tolerance": 0.1
+                }
+            },
+            {
+                "metric_id": "eeg_channel_set_match",
+                "type": "set_match_check",
+                "target_key": "eeg_channels",
+                "weight": 70,
+                "params": {
+                    "gt_value": [
+                        "EEG Fp1-LER",
+                        "EEG Fp2-LER",
+                        "EEG F7-LER",
+                        "EEG F8-LER",
+                        "EEG F3-LER",
+                        "EEG F4-LER",
+                        "EEG T3-LER",
+                        "EEG T4-LER",
+                        "EEG C3-LER",
+                        "EEG C4-LER",
+                        "EEG T5-LER",
+                        "EEG T6-LER",
+                        "EEG P3-LER",
+                        "EEG P4-LER",
+                        "EEG O1-LER",
+                        "EEG O2-LER",
+                        "EEG Fz-LER",
+                        "EEG Cz-LER",
+                        "EEG Pz-LER",
+                        "EEG Oz-LER"
+                    ],
+                    "match_mode": "exact"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case2/case2_13.json

@@ -0,0 +1,44 @@
+{
+    "meta_info": {
+        "case_id": "Physionet2018_01.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "Physionet2018"
+    },
+    "agent_input": {
+        "data_path": "data/sleep/Physionet2018_01.edf",
+        "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\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 or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": <integer|null>, \"eeg_channels\": <array|null>}",
+        "metrics": [
+            {
+                "metric_id": "channel_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "channel_count",
+                "weight": 30,
+                "params": {
+                    "gt_value": 13,
+                    "tolerance": 0.1
+                }
+            },
+            {
+                "metric_id": "eeg_channel_set_match",
+                "type": "set_match_check",
+                "target_key": "eeg_channels",
+                "weight": 70,
+                "params": {
+                    "gt_value": [
+                        "F3-M2",
+                        "F4-M1",
+                        "C3-M2",
+                        "C4-M1",
+                        "O1-M2",
+                        "O2-M1"
+                    ],
+                    "match_mode": "exact"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case2/case2_14.json

@@ -0,0 +1,44 @@
+{
+    "meta_info": {
+        "case_id": "Physionet2018_02.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "Physionet2018"
+    },
+    "agent_input": {
+        "data_path": "data/sleep/Physionet2018_02.edf",
+        "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\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 or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": <integer|null>, \"eeg_channels\": <array|null>}",
+        "metrics": [
+            {
+                "metric_id": "channel_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "channel_count",
+                "weight": 30,
+                "params": {
+                    "gt_value": 13,
+                    "tolerance": 0.1
+                }
+            },
+            {
+                "metric_id": "eeg_channel_set_match",
+                "type": "set_match_check",
+                "target_key": "eeg_channels",
+                "weight": 70,
+                "params": {
+                    "gt_value": [
+                        "F3-M2",
+                        "F4-M1",
+                        "C3-M2",
+                        "C4-M1",
+                        "O1-M2",
+                        "O2-M1"
+                    ],
+                    "match_mode": "exact"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case2/case2_15.json

@@ -0,0 +1,44 @@
+{
+    "meta_info": {
+        "case_id": "Physionet2018_03.edf",
+        "bench_subset": "NeuroBench-Sleep",
+        "difficult": 1,
+        "original_dataset": "Physionet2018"
+    },
+    "agent_input": {
+        "data_path": "data/sleep/Physionet2018_03.edf",
+        "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\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 or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": <integer|null>, \"eeg_channels\": <array|null>}",
+        "metrics": [
+            {
+                "metric_id": "channel_count_accuracy",
+                "type": "numeric_check",
+                "target_key": "channel_count",
+                "weight": 30,
+                "params": {
+                    "gt_value": 13,
+                    "tolerance": 0.1
+                }
+            },
+            {
+                "metric_id": "eeg_channel_set_match",
+                "type": "set_match_check",
+                "target_key": "eeg_channels",
+                "weight": 70,
+                "params": {
+                    "gt_value": [
+                        "F3-M2",
+                        "F4-M1",
+                        "C3-M2",
+                        "C4-M1",
+                        "O1-M2",
+                        "O2-M1"
+                    ],
+                    "match_mode": "exact"
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case3/case3_01.json

@@ -0,0 +1,47 @@
+{
+    "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 three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\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 in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": <float|null>, \"tib_min\": <float|null>, \"se_percent\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "tst_accuracy",
+                "type": "numeric_check",
+                "target_key": "tst_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 308.0,
+                    "tolerance": 9.24
+                }
+            },
+            {
+                "metric_id": "tib_accuracy",
+                "type": "numeric_check",
+                "target_key": "tib_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 440.0,
+                    "tolerance": 13.2
+                }
+            },
+            {
+                "metric_id": "se_accuracy",
+                "type": "numeric_check",
+                "target_key": "se_percent",
+                "weight": 40,
+                "params": {
+                    "gt_value": 70.0,
+                    "tolerance": 2.1
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case3/case3_02.json

@@ -0,0 +1,47 @@
+{
+    "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 three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\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 in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": <float|null>, \"tib_min\": <float|null>, \"se_percent\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "tst_accuracy",
+                "type": "numeric_check",
+                "target_key": "tst_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 359.5,
+                    "tolerance": 10.785
+                }
+            },
+            {
+                "metric_id": "tib_accuracy",
+                "type": "numeric_check",
+                "target_key": "tib_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 482.0,
+                    "tolerance": 14.459999999999999
+                }
+            },
+            {
+                "metric_id": "se_accuracy",
+                "type": "numeric_check",
+                "target_key": "se_percent",
+                "weight": 40,
+                "params": {
+                    "gt_value": 74.5850622406639,
+                    "tolerance": 2.237551867219917
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case3/case3_03.json

@@ -0,0 +1,47 @@
+{
+    "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 three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\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 in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": <float|null>, \"tib_min\": <float|null>, \"se_percent\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "tst_accuracy",
+                "type": "numeric_check",
+                "target_key": "tst_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 405.5,
+                    "tolerance": 12.165
+                }
+            },
+            {
+                "metric_id": "tib_accuracy",
+                "type": "numeric_check",
+                "target_key": "tib_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 471.5,
+                    "tolerance": 14.145
+                }
+            },
+            {
+                "metric_id": "se_accuracy",
+                "type": "numeric_check",
+                "target_key": "se_percent",
+                "weight": 40,
+                "params": {
+                    "gt_value": 86.00212089077412,
+                    "tolerance": 2.5800636267232235
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case3/case3_04.json

@@ -0,0 +1,47 @@
+{
+    "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 three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\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 in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": <float|null>, \"tib_min\": <float|null>, \"se_percent\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "tst_accuracy",
+                "type": "numeric_check",
+                "target_key": "tst_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 467.5,
+                    "tolerance": 14.025
+                }
+            },
+            {
+                "metric_id": "tib_accuracy",
+                "type": "numeric_check",
+                "target_key": "tib_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 481.5,
+                    "tolerance": 14.445
+                }
+            },
+            {
+                "metric_id": "se_accuracy",
+                "type": "numeric_check",
+                "target_key": "se_percent",
+                "weight": 40,
+                "params": {
+                    "gt_value": 97.09241952232607,
+                    "tolerance": 2.912772585669782
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case3/case3_05.json

@@ -0,0 +1,47 @@
+{
+    "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 three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\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 in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": <float|null>, \"tib_min\": <float|null>, \"se_percent\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "tst_accuracy",
+                "type": "numeric_check",
+                "target_key": "tst_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 289.5,
+                    "tolerance": 8.685
+                }
+            },
+            {
+                "metric_id": "tib_accuracy",
+                "type": "numeric_check",
+                "target_key": "tib_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 437.5,
+                    "tolerance": 13.125
+                }
+            },
+            {
+                "metric_id": "se_accuracy",
+                "type": "numeric_check",
+                "target_key": "se_percent",
+                "weight": 40,
+                "params": {
+                    "gt_value": 66.17142857142856,
+                    "tolerance": 1.9851428571428569
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case3/case3_06.json

@@ -0,0 +1,47 @@
+{
+    "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 three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\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 in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": <float|null>, \"tib_min\": <float|null>, \"se_percent\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "tst_accuracy",
+                "type": "numeric_check",
+                "target_key": "tst_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 351.5,
+                    "tolerance": 10.545
+                }
+            },
+            {
+                "metric_id": "tib_accuracy",
+                "type": "numeric_check",
+                "target_key": "tib_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 427.0,
+                    "tolerance": 12.809999999999999
+                }
+            },
+            {
+                "metric_id": "se_accuracy",
+                "type": "numeric_check",
+                "target_key": "se_percent",
+                "weight": 40,
+                "params": {
+                    "gt_value": 82.31850117096019,
+                    "tolerance": 2.469555035128806
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case3/case3_07.json

@@ -0,0 +1,47 @@
+{
+    "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 three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\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 in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": <float|null>, \"tib_min\": <float|null>, \"se_percent\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "tst_accuracy",
+                "type": "numeric_check",
+                "target_key": "tst_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 387.0,
+                    "tolerance": 11.61
+                }
+            },
+            {
+                "metric_id": "tib_accuracy",
+                "type": "numeric_check",
+                "target_key": "tib_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 428.0,
+                    "tolerance": 12.84
+                }
+            },
+            {
+                "metric_id": "se_accuracy",
+                "type": "numeric_check",
+                "target_key": "se_percent",
+                "weight": 40,
+                "params": {
+                    "gt_value": 90.42056074766354,
+                    "tolerance": 2.712616822429906
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case3/case3_08.json

@@ -0,0 +1,47 @@
+{
+    "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 three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\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 in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": <float|null>, \"tib_min\": <float|null>, \"se_percent\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "tst_accuracy",
+                "type": "numeric_check",
+                "target_key": "tst_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 275.5,
+                    "tolerance": 8.265
+                }
+            },
+            {
+                "metric_id": "tib_accuracy",
+                "type": "numeric_check",
+                "target_key": "tib_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 477.0,
+                    "tolerance": 14.309999999999999
+                }
+            },
+            {
+                "metric_id": "se_accuracy",
+                "type": "numeric_check",
+                "target_key": "se_percent",
+                "weight": 40,
+                "params": {
+                    "gt_value": 57.75681341719078,
+                    "tolerance": 1.7327044025157232
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case3/case3_09.json

@@ -0,0 +1,47 @@
+{
+    "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 three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\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 in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": <float|null>, \"tib_min\": <float|null>, \"se_percent\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "tst_accuracy",
+                "type": "numeric_check",
+                "target_key": "tst_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 468.0,
+                    "tolerance": 14.04
+                }
+            },
+            {
+                "metric_id": "tib_accuracy",
+                "type": "numeric_check",
+                "target_key": "tib_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 508.0,
+                    "tolerance": 15.24
+                }
+            },
+            {
+                "metric_id": "se_accuracy",
+                "type": "numeric_check",
+                "target_key": "se_percent",
+                "weight": 40,
+                "params": {
+                    "gt_value": 92.1259842519685,
+                    "tolerance": 2.763779527559055
+                }
+            }
+        ]
+    }
+}

sleep_assessment/cases/case3/case3_10.json

@@ -0,0 +1,47 @@
+{
+    "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 three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\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 in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": <float|null>, \"tib_min\": <float|null>, \"se_percent\": <float|null>}",
+        "metrics": [
+            {
+                "metric_id": "tst_accuracy",
+                "type": "numeric_check",
+                "target_key": "tst_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 379.0,
+                    "tolerance": 11.37
+                }
+            },
+            {
+                "metric_id": "tib_accuracy",
+                "type": "numeric_check",
+                "target_key": "tib_min",
+                "weight": 30,
+                "params": {
+                    "gt_value": 479.5,
+                    "tolerance": 14.385
+                }
+            },
+            {
+                "metric_id": "se_accuracy",
+                "type": "numeric_check",
+                "target_key": "se_percent",
+                "weight": 40,
+                "params": {
+                    "gt_value": 79.04066736183525,
+                    "tolerance": 2.3712200208550573
+                }
+            }
+        ]
+    }
+}