Entity alignment (EA) aims to identify entities referring to the same real-world object across different knowledge graphs (KGs). Recent approaches based on large language models (LLMs) typically obtain entity embeddings through knowledge representation learning and use embedding similarity to identify an alignment-uncertain entity set. For each uncertain entity, a candidate entity set (CES) is then retrieved based on embedding similarity to support subsequent alignment reasoning and decision making. However, the reliability of the CES and the reasoning capability of LLMs critically affect the effectiveness of subsequent alignment decisions. To address this issue, we propose AgentEA, a reliable EA framework based on multi-agent debate. AgentEA first improves embedding quality through entity representation preference optimization, and then introduces a two-stage multi-role debate mechanism consisting of lightweight debate verification and deep debate alignment to progressively enhance the reliability of alignment decisions while enabling more efficient debate-based reasoning. Extensive experiments on public benchmarks under cross-lingual, sparse, large-scale, and heterogeneous settings demonstrate the effectiveness of AgentEA.

翻译：实体对齐（EA）旨在识别不同知识图谱（KG）中指向同一真实世界对象的实体。基于大语言模型（LLM）的最新方法通常通过知识表示学习获取实体嵌入，并利用嵌入相似度识别对齐不确定的实体集。针对每个不确定实体，基于嵌入相似度检索候选实体集（CES），以支持后续的对齐推理与决策。然而，CES的可靠性及LLM的推理能力严重影响着后续对齐决策的有效性。为解决该问题，我们提出AgentEA——一种基于多智能体辩论的可靠EA框架。AgentEA首先通过实体表示偏好优化提升嵌入质量，随后引入由轻量级辩论验证与深度辩论对齐组成的两阶段多角色辩论机制，在实现更高效辩论推理的同时逐步增强对齐决策的可靠性。在跨语言、稀疏、大规模及异构场景下的公开基准数据集上进行的广泛实验验证了AgentEA的有效性。