ValMem: Value-Aware Memory Management for Long-Term LLM Agents

Long-horizon LLM agents rely on diverse memories to support multi-session interactions, but effective memory management requires more than retrieving semantically similar history. The agent must decide which memories to retain, compress, or discard, so that storage and reasoning resources are not spent on outdated or redundant content. Although retrieval-augmented memory systems have been developed for long-term memory management, existing methods often rely on single-level memory segmentation and retrieval. Reliance on single-level memory segmentation and retrieval makes it difficult to capture complex relationships among memories. As a result, useful evidence may be overlooked, while irrelevant content may be introduced. To overcome these limitations, we propose ValMem, a value-aware memory management framework that combines a Multi-Granularity Hierarchical Memory (MGHM) structure with Value-Driven Training (VDT). MGHM organizes interaction history along temporal and granularity dimensions, providing memory units ranging from raw evidence to compact abstractions. To better identify the latent value of memory, VDT assigns credit to memory operations by optimizing downstream task utility jointly with memory cost. Experiments on three long-horizon memory benchmarks demonstrate that ValMem outperforms current state-of-the-art methods on multi-session question answering and personalized response tasks, with consistent gains across factual recall, multi-hop reasoning, temporal reasoning, and preference inference.