Long-form criminal-law generation requires factual interpretation, issue selection, and structured formulation of legal conclusions across offense elements and general doctrines. But existing neuro-symbolic approaches encode only task-specific decision rules, not a reusable representation of criminal-law structure. We introduce CLARE, a domain-specific language for reusable criminal-law structure, which we instantiate with Korean criminal law. CLARE compiles definitions of each unit offense into an executable legal program that constrains case-time legal reasoning. The resulting program then composes case-level judgments before long-form generation. We evaluate CLARE on long-form generation and component-level Korean legal benchmarks, showing that symbolic composition improves reliability over domain-specific baselines. Our results suggest that explicitly representing criminal-law structure provides a practical foundation for reliable neuro-symbolic long-form legal generation.

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.