GLARE: Agentic Reasoning for Legal Judgment Prediction

摘要

Legal judgment prediction serves as a pivotal task in intelligent judicial systems. Although large language models have achieved remarkable progress in general reasoning, they struggle with tasks that require fine-grained distinctions between similar charges. These models often select plausible charges directly without discriminating among closely related alternatives. In this paper, we introduce GLARE, an agentic legal reasoning framework that enables models to actively retrieve and apply external knowledge during decision-making. Unlike static prediction, GLARE simulates comparative reasoning by dynamically expanding the decision space to include confusing candidates, then retrieving exclusionary logic from precedents and statutes to identify the correct judgment. Experiments on real-world datasets show that our method significantly outperforms strong baselines, especially on complex cases involving confusing or rare charges. The code is available at https://anonymous.4open.science/r/GLARE-LJP-8EDF.