HIERAMEM: Evidence-Anchored Hierarchical Memory for Self-Evolving LLM Agents

Self-evolving LLM agents improve over time by accumulating and reusing past experience through memory. However, constructing memory from prior interactions entails a fundamental tension between preserving evidence and abstracting knowledge: complete trajectories retain rich behavioral evidence but introduce substantial noise, whereas abstract memories facilitate reuse but often obscure their evidential basis. To address this tension, we introduce HIERAMEM, an evidence-anchored hierarchical memory framework that bridges complete interaction trajectories and reusable knowledge through compact, traceable evidence anchors. During memory formation, HIERAMEM contrasts related trajectories with different task outcomes to identify behaviorally discriminative action–observation evidence and retain it as evidence anchors. These anchors constrain the abstraction of transferable decision knowledge, forming a three-level hierarchy comprising episodic evidence, evidence anchors, and reusable memory. As experience accumulates, this hierarchy evolves across episodes while keeping reusable knowledge grounded in its supporting anchors. At inference time, the same anchors support memory retrieval at two granularities, serving as global references at the task level and as local addresses at the decision level. Experiments across five benchmarks show that HIERAMEM improves overall agent performance over existing memory systems, demonstrating the effectiveness of evidence anchoring in connecting memory abstraction with reliable reuse.