Beyond Leaderboards: A Method for Application-Specific Capability Profiling of Large Language Models
Public leaderboards rank large language models (LLMs) in general, but an engineer has to choose a model for a specific application, architecture and cost. This paper presents a method for application-specific capability profiling of LLMs and its realization as a containerized testbed. The method treats each application as a unit of extension: an Oracle-to-PostgreSQL migration agent, a documentation-grounded chatbot or an enterprise-support automation agent runs in its own container behind a small contract, brings its own dataset, evaluator and owned attributes, and is onboarded together with its dashboards. Measurements accumulate in a versioned, sparse metamodel of every model. The metho...
| Authors | Oleg Grynets, Vasyl Lyashkevych, Andrii Salyk et al. |
|---|---|
| Primary category | cs.LO |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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