CORAL: Context-Oriented Retention with Anchor-Based Long-Term Memory

Large language models (LLMs) increasingly rely on long-term conversational memory to maintain consistency across multi-turn and multi-session interactions. However, continuously accumulating uncompressed conversational histories introduces substantial redundancy, making task-relevant information increasingly difficult to retrieve and utilize. To address these challenges, we introduce CORAL (Context-Oriented Retention with Anchor-Based Long-Term Memory), a Multi-Agent System for constructing compact and retrieval-efficient conversational memory. CORAL first extracts salient information as structured anchors and then uses these anchors to guide LLM-based compression under a constrained memory budget. The resulting memory is indexed using Retrieval-Augmented Generation (RAG), enabling relevant evidence to be retrieved for downstream reasoning. By separating information preservation, compression, and query-time reasoning, CORAL aims to retain information with high future utility while removing repetitive or low-value conversational content. We evaluate CORAL on LoCoMo using question answering and event summarization, and systematically analyze factors such as compression fraction, compressor size, compression method, and model family. Overall, our results show that effective conversational memory compression requires preserving the right information, rather than simply retaining more content or using larger models. The code is anonymously available online.