Continuous Reorganization and Performance Preservation of Agent Memory Structure Under Distributed Change Environments

To address the core shortcomings of existing memory reorganization algorithms in distributed change environments, such as trigger lag, performance fluctuations, and memory management imbalances, a Distribution-Aware Continuous Memory Reorganization (DAMCR) algorithm is proposed. This algorithm uses “distribution awareness – dynamic reorganization – performance feedback” as its core logic, employing a four-module architecture. It constructs a distribution change awareness factor by improving KL divergence, achieves adaptive memory unit management based on multi-dimensional value assessment, and combines a buffer mechanism and performance closed-loop feedback to ensure smooth reorganization and stable performance. Experimental scenarios are conducted using gradual and abrupt distribution changes, based on CIFAR-100 and a self-built navigation dataset, and compared with eight mainstream/classic algorithms (ST, AR, DIR, ADWIN, DDM, LRU, Reservoir, PER). Experimental results show that the DAMCR algorithm achieves an average decision accuracy of 92.3%, a maximum improvement of 6.6% compared to the comparison algorithms; the average reorganization latency is 12.7ms, a maximum reduction of 55.1%; and the performance fluctuation amplitude and memory utilization are reduced by a maximum of 73.1% and improved by 23.9% respectively compared to the comparison algorithms. Ablation experiments validated the necessity of each core module of DAMCR, and statistical significance analysis (independent samples t-tests, one-way ANOVA, 95% confidence interval/variance) with Cohen’s d effect size confirmed that the performance advantage of DAMCR is statistically significant (p<0.05, Cohen’s d>1.2). This algorithm can effectively optimize the memory structure of agents under distributed change environments while maintaining their performance, providing technical support for related engineering applications.