Co-adaptive Rule Routing and Constraining for Structured Knowledge Retrieval

Structured knowledge retrieval aims to acquire task-relevant evidence from structured knowledge sources for downstream task execution. Existing methods either rely on unconstrained retrieval or predefined query-rule supervision (e.g., author–paper for seeking papers in scientific discovery and anatomy–drug for precision medicine in biomedical analysis), which is rarely available in practice. To address this limitation, we propose Co-Adaptive Rule Routing and Constraining Retrieval (CoRE), a framework that alternately determines which rule to route each query to and how to retrieve under that rule without requiring predefined query–rule pairs for training supervision. Specifically, a routing agent assigns each query to a reasoning rule, and the corresponding retriever traverses the structured knowledge base and ranks evidence that satisfies the rule’s constraints. Since the rule-routing agent and rule-constraining retriever are inherently interdependent, we formulate an alternate optimization that couples reinforcement learning for rule routing with contrastive learning for the rule-constraining retriever. This closed-loop learning enables both components to co-adapt without explicit paired query–rule supervision. Experiments on STaRK-QA datasets, spanning scientific discovery, biomedical reasoning, and E-commerce enterprise knowledge management domains, verify the superiority of our proposed CoRE. Ablations validate co-adapting both router and retriever, and generation-based routing demonstrates cross-dataset transferability and benefits from multi-domain pretraining with target-specific fine-tuning. Furthermore, marginalizing the routing policy over all queries of each dataset reveals the distribution of rule usage, informing automatic rule refinement and discovery.