Same Memory, Different Interpretations : Multi-Form Memory Retrieval with Coarse-to-Fine Reranking

Personalized memory agents aim to identify relevant information from long-term conversational histories that can support responses to the query. However, long conversation histories often contain large amounts of redundant information, making it difficult for large language models (LLMs) to capture the relevant information within a limited context window. Existing methods organize long histories into compact memories, but their fixed memory forms are limited in capturing different views of the same memory. To address these limitations, we propose **Sa**me **M**emory, Different **I**nterpretations, a multi-form memory retrieval framework with coarse-to-fine reranking. Specifically, we first construct multiple memory forms to capture factual, thematic, and contextual views of historical memories. We then retrieve each form independently and project the retrieved units back to their source memories to form a unified candidate set. Finally, we perform coarse-to-fine reranking by first establishing an overall ranking of the candidates and then adaptively refining the top-ranked memories using complementary ranking information. Extensive experiments conducted on three long-term memory datasets demonstrate that our **SaMI** framework outperforms the state-of-the-art competitors.