The Scaling Laws of Skills in LLM Agent Systems
A study reveals scaling laws for skill libraries in LLM agent systems: routing accuracy decays logarithmically with library size, while correct execution can improve downstream decisions by ~4×. Optimization raises routing accuracy from 71.3% to 91.7% and reduces hijack rate from 22.4% to 4.1%.
[2605.16508] The Scaling Laws of Skills in LLM Agent Systems
[Submitted on 15 May 2026]
Title:The Scaling Laws of Skills in LLM Agent Systems
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Abstract:As agent systems scale, skills accumulate into large reusable libraries, yet their scaling laws remain poorly understood. Across 15 frontier LLMs, 1,141 real-world skills, and over 3M routing or execution decisions, we identify two coupled laws. Routing law: single-step routing accuracy decays logarithmically with library size ($R^2{>}0.97$ for all models), with errors progressing from local skill competition to cross-family drift and capture by overly general "black-hole skills". Execution law: before state realization, joint routing is approximately multiplicative, whereas correct execution can improve difficult downstream decisions by about $4{\times}$. A single parameter, the routing logarithmic decay slope $b$, couples the two laws: routing-side fits predict execution-side rescue across models, showing that the same library property controls both pre-execution collapse and downstream recoverability. The laws are actionable: law-guided optimization raises held-out routing accuracy from 71.3% to 91.7%, reduces hijack from 22.4% to 4.1%, and transfers directionally to downstream ClawBench and ClawMark execution settings, improving mean pass rate from 49.3% to 61.6% on ClawBench and from 28.4% to 34.5% on ClawMark. These results show that agent performance depends not only on model capability, but also on the structure, granularity, and exposure policy of the skill library.
Comments: Technical Report
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.16508 [cs.CL]
(or arXiv:2605.16508v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.16508
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Qiguang Chen [view email] [v1] Fri, 15 May 2026 18:05:21 UTC (2,202 KB)
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