Abstract
Hierarchical review workflows, where a second-tier reviewer (Checker) corrects first-tier (Maker) decisions, generate valuable correction signals that encode why initial judgments failed. However, learning from these signals is hindered by information asymmetry: corrections often depend on verification actions unavailable to Makers or automated systems. We address this challenge by introducing explicit action modeling as an inferential constraint that grounds reasoning in verifiable operations rather than unconstrained text generation. We propose the Evidence-Action-Factor-Decision (EAFD) schema, a minimal representation for adjudication reasoning that prevents hallucination through operational grounding and enables learning from correction signals via explicit conflict modeling. Building on this schema, we develop a conflict-aware graph reasoning framework that: (1) constructs EAFD graphs from historical cases capturing Maker-Checker disagreements, (2) aggregates them into a retrievable knowledge base, and (3) performs top-down deductive reasoning for new cases by projecting validated resolution paths from precedents. A distinctive capability is the Request More Information (RMI) outcome: when evidence is insufficient, the system identifies precisely which verification actions remain unexecuted and generates targeted information requests. We evaluate the framework in large-scale e-commerce seller appeal adjudication. While a standard LLM-only baseline achieves only 70.8% alignment with human experts, incorporating action modeling with RMI improves alignment to 87.5%. Augmenting this with the retrieval-based knowledge graph yields the best offline performance of 95.8%. Following online deployment, the framework maintains robust performance, achieving a 96.3% alignment rate in production, demonstrating its real-world effectiveness.
Abstract (translated)
分层审查工作流中，二级审核者（Checker）对一级决策者（Maker）的判定进行修正，会生成编码初始判断为何失败的有价值修正信号。然而，从这些信号中学习受到信息不对称的阻碍：修正往往依赖于制作者或自动化系统无法获取的验证操作。我们通过引入显式操作建模作为推理约束来解决这一挑战，将推理锚定在可验证操作而非无约束的文本生成中。我们提出了证据-行动-因素-决策（EAFD）模式，这是一种用于裁决推理的最小化表示，通过操作锚定防止幻觉，并通过显式冲突建模实现从修正信号中学习。基于该模式，我们开发了一个冲突感知图推理框架，该框架：(1) 从历史案例中构建捕获制作者-审核者分歧的EAFD图，(2) 将其聚合为可检索知识库，(3) 通过从先例投影已验证的解决方案路径，对新案例执行自上而下的演绎推理。一个独特能力是“申请补充信息”（RMI）结果：当证据不足时，系统能精确识别哪些验证操作尚未执行，并生成针对性的信息请求。我们在大规模电商卖家申诉裁决中评估该框架。标准纯LLM基线仅能达到70.8%的人类专家对齐率，而结合操作建模与RMI将对齐率提升至87.5%。再叠加基于检索的知识图谱，离线性能最优达95.8%。在线部署后，该框架保持稳健表现，生产环境中达到96.3%的对齐率，证明了其现实有效性。
URL
https://arxiv.org/abs/2603.19267
