Beyond Query-Memory Matching: Path-Aware Anchor-Guided Retrieval for Long-Term Agent Memory

LLM-based agents often rely on Retrieval-Augmented Generation (RAG)-style memory systems to leverage historical experiences. However, existing methods typically rely on direct query–memory similarity, implicitly assuming that gold evidence memories are close to the query in embedding space. This assumption overlooks the semantic gap between user queries and fine-grained agent memories, making query-memory similarity alone insufficient for reliable evidence recall. To address this limitation, we propose **PAGR**, a **P**ath-Aware **A**nchor-**G**uided Evidence **R**etrieval framework. Inspired by the spreading activation mechanism in human associative memory, **PAGR** first retrieves a small set of seed memories as initial anchors. It then expands from these anchors through both global semantic associations and local temporal neighbors. A path-aware cross-encoder selects candidate memories that complement the current retrieval path. The selected memories are accumulated as evidence and reused as anchors for subsequent hops, enabling iterative memory exploration. Extensive experiments on the LoCoMo benchmark demonstrate that **PAGR** consistently outperforms all baselines, achieving the highest overall F1 scores of 45.2 with GPT-4o-mini and 41.8 with Llama-3.1-8B-Instruct, while maintaining token efficiency.