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We study this challenge in clinical trial matching, a high-stakes test bed where a useful trial must both address a patient's medical needs and satisfy complex eligibility criteria.\n  We propose SatIR, a scalable constraint-based retrieval method for clinical trial matching. SatIR converts trial eligibility criteria and summaries into formal constraints, then retrieves patient--trial pairs by executing these constraints over a database. The system combines Satisfiability Modulo Theories (SMT), relational algebra, medical ontology grounding, and large language models (LLMs): formal methods provide ex","title":"SatIR: Scalable High-Recall Constraint-Satisfaction-Based Information Retrieval for Clinical Trials Matching","url":"https://arxiv.org/abs/2604.08849","vendor":"arxiv_cs_ai"},"summary":"arXiv:2604.08849v2 Announce Type: replace-cross \nAbstract: Many important retrieval problems are not merely problems of semantic similarity, but problems of constraint satisfaction: a retrieved item should be topically relevant to a query and satisfy explicit requirements involving negation, temporal conditions, numeric thresholds, exceptions, ontological relations, and incomplete evidence. 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