发表机构
Seoul National University; Korean National Police Agency（首尔大学; 韩国警察厅）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出整合对抗性律师智能体的OBJECTION推理流水线，结合含3400个真实案例的新“自然无罪”数据集，大幅降低法律判决预测模型的假有罪率，缓解有罪偏见，推进法律AI符合无罪推定原则。
AI 中文摘要
法律判决预测（Legal Judgment Prediction，LJP）模型通常在描述控方视角事实的文档上训练，现有数据集还存在严重的有罪结果标签不平衡问题，导致这些模型出现“有罪偏见”，盲目将控方叙述当作客观事实。此前采用三步推理结构或在合成无罪数据上训练的研究虽提升了整体准确率，但仍无法在推理时缓解偏见。本文提出OBJECTION，这是一种推理时的流水线，在罪名、违法性、有责性的每一步推理中整合一个对抗性律师智能体，该智能体不同于普通评判者，会在每一个推理阶段注入法律辩护论点，主动挑战模型的有罪假设。为全面评估，本文还提出包含3400个真实案例的新“自然无罪”数据集，克服了合成无罪基准的局限。测试结果显示，OBJECTION将假有罪率（False Guilty Rate，FGR）从SOTA基准的82.93%大幅降至16.69%，证明其具备实质性法律推理能力，该研究是法律人工智能符合无罪推定原则的关键进展。
英文摘要
Legal Judgment Prediction (LJP) models are typically trained on documents that describe facts from a prosecutorial perspective. Existing datasets further exhibit severe label imbalance toward guilty outcomes. Consequently, these models suffer from "Guilty Bias", blindly accepting the prosecution's narrative as objective truth. Previous studies employing three-step reasoning structures or training on synthetically generated innocence data improve overall accuracy, but they still fail to mitigate bias at inference time. In this paper, we introduce OBJECTION, an inference-time pipeline that integrates an Adversarial Lawyer Agent into each 3-step reasoning of offense, unlawfulness, and culpability. Unlike generic critics, our agent actively challenges the model's presumptions of guilt by injecting legal defense arguments at each reasoning stage. To thoroughly evaluate this, we present a new "Natural Innocent" dataset including 3.4k real-world cases, overcoming the limitations of synthetic innocence benchmarks. Test results show that OBJECTION drastically reduces the False Guilty Rate (FGR) from 82.93% (SOTA baseline) to 16.69%, proving its capability to perform substantive legal reasoning. This work denotes a key progress toward aligning Legal AI with the presumption of innocence.
CommentsAccepted to EMNLP 2026 Main Conference. Dataset: https://huggingface.co/datasets/Kcsp0042/natural-innocent
