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arxiv: 2605.31408 · v1 · pith:Z4J46PMVnew · submitted 2026-05-29 · 💻 cs.CL · cs.AI· cs.LG

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

Xiaonan Xu , Wenjing Wu This is my paper

Pith reviewed 2026-06-28 22:19 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG
keywords LLM agentsskill documentspresentation granularitySkillsBenchtask success rateabstraction levelworked examples
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The pith

Giving LLM agents skill documents raises task success by 18-36 percentage points while varying how the skills are written changes outcomes little.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether the way skill knowledge is presented to large-language-model agents affects how well they complete tasks. It runs a controlled experiment on a fixed 30-task subset of SkillsBench, comparing no-skill baselines against six versions of skill documents that differ in abstraction level and presence of examples. Two models are evaluated across five trials per cell. Skill availability produces large, consistent gains in pass rate; the granularity variations produce only small, uncertain shifts that depend on the model. A reader would care because the result points to a practical priority: supplying agents with procedural knowledge matters more than polishing its format.

Core claim

In the 30-task controlled subset, skill conditions raise mean pass rate by 26.7-36.0 points for GPT-5.5 and 18.0-26.0 points for DeepSeek V4-Flash relative to the no-skill baseline. Low-abstraction versus high-abstraction guidance differs by +0.7 points for one model and -6.7 points for the other, with both intervals crossing zero. Adding one worked example changes performance by +0.7 and +1.3 points. The same pattern holds under mean-reward checks.

What carries the argument

A pinned 30-task domain-balanced SkillsBench subset with six skill conditions (no skill plus five presentation variants) evaluated on two reasoning-enabled models over five trials per task-condition-model cell.

If this is right

  • Skill availability is the dominant lever for raising agent success on these tasks.
  • Low- versus high-abstraction guidance does not reliably alter outcomes.
  • Adding a single worked example produces negligible gains over the no-example version.
  • The pattern is preserved when performance is measured by mean reward instead of pass rate.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Agent builders could allocate effort first to creating usable skill documents and only later to tuning their wording.
  • The finding may extend to other agent benchmarks that supply procedural knowledge at inference time.
  • Larger studies could check whether granularity effects grow when tasks require more precise procedural steps.

Load-bearing premise

The chosen 30-task subset and the six skill documents were written and applied in ways that do not introduce major hidden differences between conditions.

What would settle it

Re-running the same design on a larger or differently sampled task set and finding that granularity changes produce large, consistent pass-rate differences would falsify the claim that presentation effects are small and uncertain.

Figures

Figures reproduced from arXiv: 2605.31408 by Wenjing Wu, Xiaonan Xu.

Figure 1
Figure 1. Figure 1: Task-mean pass rates by model and condition [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Primary task-paired presentation contrasts with 95% bootstrap confidence intervals 4.5. Cross-Model Consistency and Robustness RQ1 changes sign across the two model configurations, as summarized in [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper claims that in a controlled experiment on a 30-task domain-balanced subset of SkillsBench (oracle-validated), skill availability substantially boosts LLM agent pass rates (26.7–36.0 pp for GPT-5.5; 18.0–26.0 pp for DeepSeek V4-Flash) relative to no-skill baselines, while presentation-granularity manipulations (low- vs. high-abstraction guidance; medium-abstraction with vs. without one worked example) yield small effects (+0.7 to -6.7 pp) whose 95% bootstrap CIs cross zero. The design uses two reasoning-enabled models, six skill conditions, five trials per task-condition-model cell, task-level aggregation before paired contrasts, 1,800 total rows, and mean-reward robustness checks.

Significance. If the results hold, the work supplies clear empirical evidence that skill-document availability dominates over granularity choices for these models and tasks. Strengths include the pinned benchmark version, oracle validation, bootstrap CIs, task-level (not trial-level) aggregation, and explicit robustness checks; these features make the separation between large availability effects and uncertain granularity effects directly testable and reproducible within the reported scope.

major comments (1)
  1. [Methods] Methods / Experimental Design: the six skill documents and their concrete realizations under each granularity condition are not provided (nor are excerpts or links). Because the central contrast is between availability and granularity, the absence of the actual documents leaves open the possibility of unmeasured authoring confounds in how the low-/high-abstraction and example variants were written; this directly affects interpretability of the reported small, uncertain granularity deltas.
minor comments (2)
  1. The abstract and results sections could explicitly state the exact number of tasks per domain in the 30-task subset and the precise definition of the 'official oracle runs' used for validation.
  2. Table or figure captions should clarify whether the reported percentage-point differences are raw means or model-adjusted; the bootstrap procedure (number of resamples, stratification) should be stated once in the main text.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed and constructive review. The recommendation for minor revision is noted, and we address the single major comment below. We agree that the point raises a valid concern for interpretability.

read point-by-point responses

  1. Referee: [Methods] Methods / Experimental Design: the six skill documents and their concrete realizations under each granularity condition are not provided (nor are excerpts or links). Because the central contrast is between availability and granularity, the absence of the actual documents leaves open the possibility of unmeasured authoring confounds in how the low-/high-abstraction and example variants were written; this directly affects interpretability of the reported small, uncertain granularity deltas.

    Authors: We agree that the absence of the skill documents limits the ability to assess potential authoring confounds and reduces interpretability of the granularity contrasts. In the revised version we will add the full text of all six skill documents (and their low-/high-abstraction and example variants) to an appendix. We will also include a permanent link to a public repository containing the complete documents, task prompts, and experimental materials. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper reports direct experimental measurements from a controlled 30-task subset of SkillsBench, with five trials per cell, bootstrap CIs, and mean-reward checks. All claims concern observed pass-rate differences between skill-availability and granularity conditions; no derivations, equations, fitted parameters renamed as predictions, or self-citation chains appear in the load-bearing steps. The design is self-contained against the stated oracle-validated tasks and statistical contrasts.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The empirical claims rest on standard statistical practices for bootstrap intervals and on the domain assumption that the pinned 30-task subset isolates the tested variables; no free parameters or invented entities are introduced.

axioms (2)
  • standard math Bootstrap confidence intervals supply valid uncertainty estimates for task-mean pass rates aggregated over 30 tasks
    Invoked for the reported 95% CIs on granularity contrasts.
  • domain assumption The pinned SkillsBench 30-task subset and oracle validation ensure domain balance and task validity for measuring presentation effects
    Central premise allowing generalization from the reported contrasts.

pith-pipeline@v0.9.1-grok · 5814 in / 1442 out tokens · 38652 ms · 2026-06-28T22:19:42.735884+00:00 · methodology

discussion (0)

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