Legal judgments may inadvertently contain errors, yet human appellate review faces immense pressure to improve efficiency. While current legal AI focuses on legal judgment prediction or document generation, judgment review is fundamentally a retrospective diagnostic task. In this paper, we introduce Appellate Review to assess large language models’ (LLMs) legal diagnostic reasoning underlying appellate review. We specifically target Chinese criminal cases, under the assumption that their highly structured logic, which maps explicit facts to strict statutory penalties, provides a verifiable ground truth. We decompose Appellate Review into three subtasks: error detection, error classification, and error correction. To support this, we construct AR-Bench, a controlled benchmark comprising 8,700 judgments with counterfactually introduced errors spanning charges, prison terms, and fines. Evaluations of 16 LLM variants reveal substantial limitations: models classify known errors relatively well but struggle with unguided detection and precise correction. Further analyses of a representative model show that it identifies explicit textual contradictions but fundamentally fails at holistic deductive reasoning and rule-based arithmetic.

Retrieval-based code question answering seeks to match user queries in natural language to relevant code snippets. Previous approaches typically rely on pretraining models using crafted bi-modal and uni-modal datasets to align text and code representations. In this paper, we introduce ProCQA, a large-scale programming question answering dataset extracted from the StackOverflow community, offering naturally structured mixed-modal QA pairs. To validate its effectiveness, we propose a modality-agnostic contrastive pre-training approach to improve the alignment of text and code representations of current code language models. Compared to previous models that primarily employ bimodal and unimodal pairs extracted from CodeSearchNet for pre-training, our model exhibits significant performance improvements across a wide range of code retrieval benchmarks.