Imagine you are designing a large language model to act as a specialized legal assistant. The model must be able to both engage in a coherent, multi-turn dialogue about a user's current legal query and accurately pull in specific, relevant precedents from a vast database of historical case law. The model's attention mechanism has access to two sources of information: 1) a 'local memory' containing the immediate context of the current conversation, and 2) a 'retrieved memory' containing the top 'k' most relevant text passages from the case law database, found using a search process. Propose two distinct architectural strategies for how the model's attention mechanism could combine these two memory sources to generate its next response. Analyze the potential trade-offs of each strategy specifically for this legal assistant scenario, and conclude by recommending one strategy over the other, justifying why it is better suited for balancing conversational coherence with factual accuracy.