Legal Case Retrieval (LCR) identifies relevant precedent cases to support legal reasoning and sentencing. A core challenge for cross-system LCR is the structural divergence between legal systems: civil-law systems provide explicit statutory hierarchies but lack inter-case citations, while common-law systems offer rich citation networks but lack uniform codified semantic hierarchies. Existing methods exploit only one side of this divide, remaining system-specific. We propose DSLaw, which unifies both systems by implicitly modeling hierarchical semantics through residual quantization and using the resulting discrete representations as semantic anchors for cross-case graph reasoning. By progressively decomposing legal semantics from coarse concepts to fine-grained distinctions, DSLaw constructs implicit connections even without explicit citations. Experiments on COLIEE2025 (common law) and ELAM (civil law) show DSLaw consistently outperforms strong baselines, while joint training further validates cross-system generalization. The implementation is provided at https://github.com/KDEGroup/DSLaw.

Large Language Models (LLMs) demonstrate strong generation and reasoning abilities, but they still face challenges in long-term memory retention and multi-turn conversational consistency. Existing memory-augmented methods often incorporate full dialog histories without filtering, resulting in information redundancy and inference latency. Inspired by the episodic memory mechanism in human cognition, we abstract conversational context into Episodic Memory Units (EMUs). We then propose a comprehensive framework, Episodic Memory Agent (EMA), along with a filtering decision module called MemDecider. Specifically, EMA organizes and retrieves EMUs to support response generation, while MemDecider filters information to reduce noise and improve overall performance. Experiments on two widely-used benchmarks show that EMA maintains competitive performance, and integrating MemDecider into other methods reduces their token consumption by an average of 11.48% while effectively improving the overall performance. Code is available at https://github.com/Hongyi4221/EMA.