Confidence-Gated Mechanistic Bridge Retrieval for Biomedical Knowledge Graph Reasoning

Biomedical knowledge-graph reasoning requires recovering intermediate mechanistic evidence rather than only connecting a source and target. Semantic guidance can improve graph retrieval, but its value is conditional: useful candidates may be missing, semantically plausible candidates may provide limited downstream retrieval value, and the guidance strength may vary across queries. We introduce Confidence Gated Mechanistic Bridge Retrieval, a graph-constrained framework that separates these challenges into candidate coverage, retrieval utility, and query-specific influence. The method constructs graph-linked Bridges from complementary biological views, estimates retrieval-aware reliability from downstream supervision, and adaptively controls their influence during graph valid search. On DrugMechDB, the full system improves Path Success from 27.02% to 39.52% over graph-only retrieval. More importantly, controlled analyses show that the improvement does not arise from a single adaptive component: candidate coverage, downstream Bridge utility, and query-specific influence constitute distinct failure modes that require separate interventions. These results support a failure-oriented view of semantic graph guidance, in which availability, downstream value, and query-specific influence should be modeled separately.An anonymous repository for code and artifacts is available at https://anonymous.4open.science/r/iclr2027-bridge/