arXiv:2605.29440v1 Announce Type: cross Abstract: Retrieval-augmented LLM agents increasingly rely on curated skill banks: collections of reusable textual principles that guide decision making on complex tasks. Existing approaches typically expand these banks in an append-only fashion, continuously adding new skills without removing redundant, outdated, or harmful ones, resulting in inefficient and poorly curated repositories. In this paper, we formulate the skill bank curation as a constrained multi-objective problem: a desirable bank must be useful for the agent, diverse in its content, and
The proliferation of LLM agents and their increasing complexity necessitates effective methods for managing their underlying knowledge and skill bases.
Efficient and well-curated skill banks are critical for scaling agentic AI, reducing operational overhead, and ensuring reliable decision-making in complex tasks.
This research introduces a structured, multi-objective approach to skill bank curation, moving beyond simple 'append-only' methods to enable more sophisticated management of agent capabilities.
- · AI agent developers
- · Enterprises deploying LLM agents
- · AI infrastructure providers
- · Developers relying on ad-hoc skill management
- · Inefficient LLM agent systems
Improved performance and reliability of LLM agents due to better skill management.
Accelerated deployment and adoption of sophisticated AI agents across various industries.
Increased demand for tools and platforms that facilitate multi-objective skill bank curation and optimization.
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Read at arXiv cs.AI