More Skills, Worse Agents? Skill Shadowing Degrades Performance When Expanding Skill Libraries
arXiv:2605.24050v2 Announce Type: replace-cross Abstract: Skill libraries allow LLM agents to load task-specific instructions on demand, letting non-expert users solve domain-specific tasks through natural language without knowing which skills exist or how they work. However, performance degrades as libraries grow -- by up to 21\% when scaling from a small set of helpful skills to a 202-skill library. In this work, we formulate this performance degradation as the pass rate drop between loading a library of known-helpful skills and the full library. Moreover, we propose to decompose the pass ra
The rapid expansion and adoption of large language models (LLMs) and AI agents are pushing the boundaries of their practical application, revealing scale-related degradation issues.
This research highlights a fundamental challenge in scaling AI agent capabilities, suggesting that simply adding more skills can paradoxically reduce performance, impacting the trajectory of autonomous systems.
The expectation that larger skill libraries equate to better AI agent performance is now challenged, necessitating new architectural and methodological approaches for designing effective agents.
- · AI researchers focused on agent architecture
- · Companies developing sophisticated AI agent orchestration platforms
- · Users relying on niche, optimized AI agents
- · Developers pursuing naive 'more skills is better' strategies
- · Platforms aiming for vast, undifferentiated AI skill marketplaces
AI agent development will shift from purely additive skill integration to more curated, context-aware, and possibly hierarchical skill management.
This could lead to a bifurcation of AI agents into highly specialized, efficient agents and larger, more complex agents with sophisticated skill arbitration layers.
The complexity of managing agent scalability might increase AI development costs and slow the deployment of truly general-purpose autonomous agents, fostering a focus on targeted solutions.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI