ConvMemory v2: A Recall-Preserving Top-10 Evidence Reranker for Conversational Memory Retrieval

ConvMemory v2 has been introduced as a token-evidence reranker that refines the output of the ConvMemory v1 model by reordering its protected top-10 candidate set without altering the recall metrics. The model, based on a fine-tuned ms-marco-MiniLM-L-6-v2 cross-encoder with 22,713,601 parameters, demonstrates significant performance improvements on the LoCoMo conversational memory benchmark, achieving a FULL MRR of 0.6560 compared to v1's 0.5824, while maintaining identical Recall@10 and Hit@10 metrics. This development is crucial for practitioners as it showcases an effective method for enhancing retrieval quality in memory-based conversational systems without incurring the computational costs of more complex models.