CoRelMem: Coherent Relation Induction for Long-Term Agent Memory

Long-term memory is essential for LLM-based agents to accumulate and reuse information across extended interactions. Existing memory systems commonly retrieve historical information based on lexical or semantic similarity, implicitly assuming that memories more similar to the query are also more useful for answering it. However, semantic similarity does not necessarily reflect reasoning relevance. Relevant memories with weak query similarity may be overlooked, although their relevance may be revealed through associations with other retrieved memories. Meanwhile, highly similar but low-value memories may receive excessive priority and occupy the limited retrieval budget. To address this issue, we propose CoRelMem, a structured coherent relation induction framework for long-term agent memory. CoRelMem organizes long-term memories through logical association and temporal evolution, enabling retrieval to follow cross-memory dependencies and recover relevant memories that cannot be identified through direct semantic matching alone. Based on these structures, a knowledge-guided dual-path retrieval strategy expands candidate memories from complementary relational and temporal perspectives, improving the coverage of useful information. CoRelMem further introduces an information-aware reranking mechanism that considers both retrieval relevance and conditional information contribution, reducing the priority of superficially similar but less informative memories. Experiments on LoCoMo and LongMemEval demonstrate consistent improvements in both memory retrieval and downstream question answering. The code is available at https://anonymous.4open.science/r/CoRelMem-DC8B.