Skill Availability and Presentation Granularity in Large-Language-Model Agents: A Controlled SkillsBench Study
arXiv:2605.31408v1 Announce Type: cross Abstract: Skill documents provide procedural knowledge to large-language-model agents at inference time. This article studies whether the presentation granularity of controlled skill knowledge changes downstream task success. The experiment uses a pinned SkillsBench version, a 30-task domain-balanced subset validated by official oracle runs, two reasoning-enabled model configurations, six skill conditions, and five trials per task-condition-model cell. Skill availability is the clearest empirical signal. Relative to no skill, skill conditions increase ta
The proliferation of large language models and the increasing focus on their autonomous capabilities make research into agentic design a critical immediate concern.
This research provides empirical data on best practices for designing and deploying effective AI agents, which are becoming a pivotal component in collapsing workflows and automating tasks.
Our understanding of how to optimize the design and deployment of large-language-model agents, specifically regarding skill availability and presentation, is being refined.
- · AI agent developers
- · Companies adopting AI agents
- · Efficiency software providers
- · Inefficient workflow providers
- · Manual white-collar tasks
- · Poorly designed AI agents
Improved performance and broader adoption of AI agents across various industries.
Significant disruption and automation of existing white-collar job functions and SaaS layers.
The acceleration of new business models entirely dependent on highly autonomous AI systems.
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Read at arXiv cs.LG