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ARXIV:2605.31408 · AGENTS · SUBMITTED 01 JUN · 20:26 UTC · FRESHNESS STALE
ARXIV:2605.31408AGENTSSUBMITTED 01 JUN · 20:26 UTCFRESHNESS STALEXiaonan Xu · Wenjing Wu · arXiv
Skill availability significantly improves LLM agent performance, while presentation granularity has minimal impact.
Opportunity summary
Pain Skill availability significantly improves LLM agent performance, while presentation granularity has minimal impact.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Skill availability significantly improves LLM agent performance, while presentation granularity has minimal impact. This article studies whether the presentation granularity of controlled skill knowledge changes downstream task success.
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.
ScienceToStartup currently rates this 4.0/10 on the public viability pass. In this controlled subset, skill availability is associated with higher success than no skill, while the tested presentation-granularity changes yield small, uncertain, and model-dependent…
Agents moved forward this cycle; last verified June 2026. Public score 4.0/10.
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Skill availability significantly improves LLM agent performance, while presentation granularity has minimal impact.
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10.48550/arXiv.2605.31408Skill availability significantly improves LLM agent performance, while presentation granularity has minimal impact.
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 task-mean pass rate by 26.7 to 36.0 percentage points for GPT-5.5 and by 18.0 to 26.0 percentage points for DeepSeek V4-Flash. The final data contain 1,800 rows, with 900 rows for each model. The task is the inference unit. Five trials are aggregated within each task-condition-model cell before paired contrasts are estimated over 30 tasks. The primary presentation contrasts are smaller and uncertain. Low-abstraction guidance differs from high-abstraction guidance by +0.7 percentage points for GPT-5.5 and -6.7 percentage points for DeepSeek V4-Flash, with both 95% bootstrap confidence intervals crossing zero. Adding one worked example to medium-abstraction guidance differs from the no-example variant by +0.7 and +1.3 percentage points. Mean-reward robustness checks preserve the same substantive conclusion. In this controlled subset, skill availability is associated with higher success than no skill, while the tested presentation-granularity changes yield small, uncertain, and model-dependent effects.
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PROBLEM
Skill availability significantly improves LLM agent performance, while presentation granularity has minimal impact. This article studies whether the presentation granularity of controlled skill knowledge changes downstream task success.
METHOD
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.
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. In this controlled subset, skill availability is associated with higher success than no skill, while the tested presentation-granularity changes yield small, uncertain, and model-dependent effects.
WHY NOW
Agents moved forward this cycle; last verified June 2026. Public score 4.0/10.
{"file name": "input.pdf", "number of pages": 13, "author": "Xiaonan Xu; Wenjing Wu", "title": "Skill Availability and Presentation Granularity in Large-Language-Model Agents: A Controlled SkillsBench Study"
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Skill availability significantly improves LLM agent performance, while presentation granularity has minimal impact.
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4.0/10 public viability
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