Retrieval-Driven Memory Reconsolidation for Long-Term LLM Agents

Long-term memory is essential for LLM-based agents operating over extended interactions. Many existing systems primarily update memory when new information arrives, treating retrieval as the endpoint of memory access rather than a driver of memory evolution. Recent methods have begun to exploit signals from memory use, but it remains less explored how retrieved evidence can guide agent-selected edits to a typed memory structure. Inspired by memory reconsolidation in cognitive neuroscience, we propose REALM, a reconsolidation-evolution agentic long-term memory framework. REALM organizes experiences into a heterogeneous cognitive graph, retrieves evidence through adaptively composed graph-search atoms, and performs retrieval-driven reconsolidation by using the activated subgraph and interaction feedback to add, strengthen, or weaken typed relations. Under our evaluation protocol, REALM achieves average accuracies of 75.97% on LoCoMo and 65.11% on LongMemEval, corresponding to gains of 7.17 and 1.90 points over the strongest reported baseline averages. Ablation studies show that memory reconsolidation improves overall performance on both benchmarks, while further analyses indicate that it progressively reorganizes related memory units into more coherent local structures for collective evidence recall and utilization during reasoning. These results support retrieval-driven reconsolidation as one mechanism for adaptive long-term memory in LLM agents.