@inproceedings{Zhang2026,
 title = {{{UCDCS}} at {{COLIEE}} 2026: {{A Multi-Stage Framework}} for {{Legal Case Retrieval}} via {{Structural Abstraction}} and {{Specialised LLMs}}},
 booktitle = {Proceedings of the {{Workshop}} on the {{Thirteenth International Competition}} on {{Legal Information Extraction}} and {{Entailment}} ({{COLIEE}} 2026)},
 author = {Zhang, Yuchen and Lillis, David},
 year = {2026},
 abstract = {Automated legal case retrieval is a critical yet challenging task due to the extreme length of judicial documents and the complexity of judicial reasoning. In this paper, we present our multi-stage framework for the COLIEE 2026 Task 1 (Legal Case Retrieval). To address the challenges of long-form legal texts, we first employ a structural abstraction strategy that distills cases into key factual and logical components. Our retrieval pipeline utilises a hybrid strategy combining BM25 with BGE-M3 dense embeddings, establishing a high-recall foundation ({$\approx$}0.85). For the subsequent re-ranking stage, we move beyond general-purpose semantic matching by leveraging fine-tuned MonoT5 models and SaulLM-7B, a specialised legal large language model. This transition allows the system to prioritise logic over surface-level topical similarity. Among the three runs we submitted, the best performance achieved by the proposed framework reached a final precision of 0.2480 and an F1-score of 0.2645 on the evaluation set, improving upon the retrieval-only baselines. These results indicate that combining hybrid retrieval with domain-adapted re-ranking is a promising approach.},
 langid = {english},
}