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This study introduces CondMedQA, a new benchmark and Condition-Gated Reasoning framework that explicitly models patient-specific variables like comorbidities to improve biomedical QA accuracy. By selectively activating reasoning paths based on query conditions, the approach addresses a critical gap where existing systems fail to account for context-dependent clinical factors. The results demonstrate that explicitly modeling conditionality is essential for achieving robust medical reasoning without sacrificing state-of-the-art performance.
Condition-Gated Reasoning for Context-Dependent Biomedical Question Answering
arXiv:2602.17911v3 Announce Type: replace Abstract: Current biomedical question answering (QA) systems often assume that medical knowledge applies uniformly, yet real-world clinical reasoning is inherently conditional: nearly every decision depends on patient-specific factors such as comorbidities and contraindications. Existing benchmarks do not evaluate such conditional reasoning, and retrieval-augmented or graph-based methods lack explicit mechanisms to ensure that retrieved knowledge is applicable to given context. To address this gap, we propose CondMedQA, the first benchmark for conditional biomedical QA, consisting of multi-hop questions whose answers vary with patient conditions. Furthermore, we propose Condition-Gated Reasoning (CGR), a novel framework that constructs condition-aware knowledge graphs and selectively activates or prunes reasoning paths based on query conditions. Our findings show that CGR more reliably selects condition-appropriate answers while matching or exceeding state-of-the-art performance on biomedical QA benchmarks, highlighting the importance of explicitly modeling conditionality for robust medical reasoning.
Abstract:Current biomedical question answering (QA) systems often assume that medical knowledge applies uniformly, yet real-world clinical reasoning is inherently conditional: nearly every decision depends on patient-specific factors such as comorbidities and contraindications. Existing benchmarks do not evaluate such conditional reasoning, and retrieval-augmented or graph-based methods lack explicit mechanisms to ensure that retrieved knowledge is applicable to given context. To address this gap, we propose CondMedQA, the first benchmark for conditional biomedical QA, consisting of multi-hop questions whose answers vary with patient conditions. Furthermore, we propose Condition-Gated Reasoning (CGR), a novel framework that constructs condition-aware knowledge graphs and selectively activates or prunes reasoning paths based on query conditions. Our findings show that CGR more reliably selects condition-appropriate answers while matching or exceeding state-of-the-art performance on biomedical QA benchmarks, highlighting the importance of explicitly modeling conditionality for robust medical reasoning.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2602.17911 [cs.CL]
(or arXiv:2602.17911v3 [cs.CL] for this version)
https://doi.org/10.1145/3770855.3818963 https://doi.org/10.1145/3770855.3818963 https://doi.org/10.1145/3770855.3818963 https://doi.org/10.1145/3770855.3818963 https://doi.org/10.1145/3770855.3818963 DOI(s) linking to related resources
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