CORA: Capability-Oriented Region Aggregation for Experiential Memory in LLM Agents

Large language model agents increasingly accumulate rich experiential memories, yet reusing this experience under task diversity remains a central challenge. Similarity-based retrieval selects memories by topical resemblance, but its ranking signal contains no information about previous task outcomes. Utility-aware retrieval incorporates outcome-based usefulness, but a scalar utility score combines results from different task categories into one value, while estimating utility independently for each memory makes sparse feedback noisy and prevents evidence sharing across memories with similar behavior. We therefore introduce Capability-Oriented Region Aggregation (CORA), which represents each memory by a category-conditioned utility profile and groups memories with similar profiles into capability-oriented regions. CORA comprises three mechanisms: Capability Profile Clustering discovers these regions; Hierarchical Utility Estimation combines individual and regional utility for category-specific reranking; and Region-Level Knowledge Aggregation consolidates recurring failure patterns within capability-similar regions into retrieval-time guidance. Experiments across four benchmarks and five frozen language-model backbones show that CORA achieves final-test gains of up to 6.43 percentage points over the strongest competing method, demonstrating the effectiveness of capability-oriented regions for organizing and reusing experiential memory across diverse tasks.