Effective Knowledge Conflict Detection via Joint Agentic Optimization

Knowledge conflict detection is a fundamental challenge for large language model (LLM) systems that rely on external knowledge. Existing approaches address this problem either with end-to-end supervised models or with multi-stage pipelines dependent on fixed heuristics. However, supervised models often generalize poorly to diverse conflict patterns, whereas heuristic pipelines rely on fixed rules and introduce considerable computational overhead. To overcome these limitations, we propose $\textbf{G}$rouping and $\textbf{C}$onflict $\textbf{D}$etection ($\mathsf{GCD}$), a two-stage agentic framework for knowledge conflict detection that aligns the detection process with the inherent sparsity of knowledge conflicts, thereby reducing the decision space and enabling fine-grained conflict detection. $\mathsf{GCD}$ decomposes knowledge conflict detection into two subtasks and employs two cooperative agents to address them: a Knowledge Grouping Agent (G-Agent), which creates conflict-preserving groups of textual units with potential conflicts, and an Intra-group Conflict Detection Agent (D-Agent), which detects conflicts within each group. The G-Agent's grouping policy is not fixed in advance but is jointly trained with D-Agent via a shared global reward, enabling grouping to be explicitly optimized for accurate detection rather than relying on fixed heuristics. Extensive experiments on five benchmarks demonstrate that $\mathsf{GCD}$ consistently outperforms SOTA methods, while reducing inference latency and token consumption.