Skill Availability and Presentation Granularity in Large-Language-Model Agents: A Controlled SkillsBench Study

📰 ArXiv cs.AI

Learn how skill availability and presentation granularity impact large-language-model agents' task success in a controlled SkillsBench study

advanced Published 1 Jun 2026

Action Steps

Configure a SkillsBench environment with a pinned version and a domain-balanced task subset
Design an experiment with multiple skill conditions and model configurations to test presentation granularity
Run trials for each task-condition-model cell and evaluate downstream task success
Analyze the results to determine the impact of skill availability and presentation granularity on task performance
Apply the findings to optimize the design of skill documents and improve the overall performance of large-language-model agents

Who Needs to Know This

NLP engineers and researchers can benefit from this study to improve the performance of large-language-model agents in various tasks

Key Insight

💡 The presentation granularity of controlled skill knowledge can significantly impact downstream task success in large-language-model agents

Key Takeaways

Learn how skill availability and presentation granularity impact large-language-model agents' task success in a controlled SkillsBench study

Full Article

Title: Skill Availability and Presentation Granularity in Large-Language-Model Agents: A Controlled SkillsBench Study

Abstract:
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. Ski

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