COVE: Adaptive Memory-Parameter Coordination for LLM Self-Evolution

The paper presents COVE, a unified framework for LLM-agent self-evolution that coordinates two learning channels: harness-based updates, such as editable memories and skills, and parameter-based learning that internalizes experience into model weights. COVE uses task-aware routing, stage-aware scheduling, and knowledge optimization to match task or knowledge types with the appropriate channel. The authors report evaluations across multiple task categories, claiming more robust and efficient improvement than single-channel evolution strategies in changing environments. The abstract does not provide detailed benchmarks, baselines, model sizes, or quantitative results.