CaseTCM: A Benchmark for Case-Preserving Cross-Source Retrieval in Traditional Chinese Medicine

Question answering in traditional Chinese medicine (TCM) depends on links between disease-monograph pathology reasoning and pharmacopoeia herb grounding, and a usable answer has to remain traceable to the evidence route a clinician would recognize. Existing medical and TCM benchmarks, however, grade closed-book answers against a key: they ask whether a final string is right, not whether a system can recover the source case that underwrites it and cross between two structurally different sources while keeping that case intact. We present CaseTCM, a benchmark for case-preserving cross-source retrieval in TCM. It comprises $3,646$ pathology questions drawn from 17 disease monographs, graded against slot-structured gold answers taken verbatim from source fields, together with $1,237$ herb-name-masked, candidate-constrained reverse-bridge probes over five books; the frozen benchmark files ship with a matching protocol of shared prompts, one parser, routing controls, a paraphrase robustness probe and an error taxonomy. Evaluating dense, sparse and graph retrieval, a structured RAG comparator and a deterministic reference system shows that the ceiling here is set by preserving the source case template rather than by retrieval scale: flat retrieval plateaus far below the reference system, typed field scoring adds nothing once candidates are bounded by the routed case, exact-key routing is paraphrase-brittle until a lexical fallback is added, and almost all remaining errors are wrong-case routing rather than answer realization. We anticipate that CaseTCM will support retrieval systems whose evidence routes can be inspected field by field. The benchmark files, the evaluation protocol and the reference implementation are released at https://anonymous.4open.science/r/Case-tcm-code.