COVE: A New Framework for Adaptive Self-Evolution in LLM Agents
A recent paper published on arXiv (2608.01234) presents COVE, a framework designed for the self-evolution of large language model (LLM) agents, which combines harness-based and parameter-based learning approaches. This framework tackles the difficulties faced by LLM agents in ever-changing environments where tools and user requirements evolve after deployment. Current techniques either rely on external feedback for quick adjustments or focus on internalizing experiences for more profound enhancements, leading to a compromise between adaptability and efficiency. COVE merges these strategies using task-aware routing, stage-aware scheduling, and knowledge optimization, viewing self-evolution as a synchronized process. While the paper details COVE's conceptual framework, it lacks experimental data or benchmarks.
Key facts
- Paper titled 'Learning What to Remember and What to Internalize in LLM Self-Evolution via Adaptive Memory-Parameter Coordination'
- Published on arXiv with identifier 2608.01234v1
- Introduces COVE, a unified agent self-evolution framework
- Combines harness-based and parameter-based learning
- Uses task-aware routing, stage-aware scheduling, and knowledge optimization
- Addresses dynamic environments where tool interfaces, APIs, and user requirements change
- Aims to balance flexibility and performance in LLM agents
- Treats self-evolution as a coordinated process, not indiscriminate accumulation
Entities
Institutions
- arXiv