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  **CLIR-Bench** is an evidence-auditable benchmark for **multimodal question answering over irregular clinical time series**, constructed from de-identified ICU records in MIMIC-IV. It contains 6,600 multiple-choice QA instances spanning 11 clinical variables and intervention signals, organized into four core capability dimensions: temporal understanding, reasoning, forecasting, and decision-making. Each question is explicitly linked to timestamp-level evidence and task-specific answer derivation rules, enabling comprehensive evaluation not only of answer accuracy but also of evidence faithfulness, evidence reliance, and causal sensitivity. By focusing on sparse, asynchronous, missing, and long-horizon clinical observations, CLIR-Bench provides a realistic and diagnostic testbed for assessing whether models can reliably identify and reason over patient-specific temporal evidence.
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- > ⚠️ **Note:** The current version does **not include time-series data derived from MIMIC-IV and time-series evidence**.
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  > Access to data involving MIMIC-IV-related tasks requires additional credentialing and approval from PhysioNet.
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  > Researchers can apply for access via the official website: [MIMIC-IV](https://physionet.org/content/mimiciv/).
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  **CLIR-Bench** is an evidence-auditable benchmark for **multimodal question answering over irregular clinical time series**, constructed from de-identified ICU records in MIMIC-IV. It contains 6,600 multiple-choice QA instances spanning 11 clinical variables and intervention signals, organized into four core capability dimensions: temporal understanding, reasoning, forecasting, and decision-making. Each question is explicitly linked to timestamp-level evidence and task-specific answer derivation rules, enabling comprehensive evaluation not only of answer accuracy but also of evidence faithfulness, evidence reliance, and causal sensitivity. By focusing on sparse, asynchronous, missing, and long-horizon clinical observations, CLIR-Bench provides a realistic and diagnostic testbed for assessing whether models can reliably identify and reason over patient-specific temporal evidence.
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+ > ⚠️ **Note:** The current version does **not include time-series data and evidence derived from MIMIC-IV**.
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  > Access to data involving MIMIC-IV-related tasks requires additional credentialing and approval from PhysioNet.
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  > Researchers can apply for access via the official website: [MIMIC-IV](https://physionet.org/content/mimiciv/).
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