pith:RGCV7AB6
RouteProfile: Graph-Based Profiling for Cold-Start LLM Routing
Structured profiles for LLM capabilities outperform flat ones in routing tasks and improve generalization to new models.
arxiv:2605.00180 v2 · 2026-04-30 · cs.NI · cs.CL
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Claims
Through systematic evaluation across three representative routers under both standard and new-LLM generalization settings, we show that: (1) structured profiles consistently outperform flat ones; (2) query-level signals are more reliable than coarse domain-level signals; and (3) generalization to newly introduced models benefits most from structured profiles under trainable configurations.
That evaluations on three routers and the chosen standard plus generalization settings are representative enough to elucidate the full design space and apply to other routing systems and LLMs.
RouteProfile organizes LLM profile design into organizational form, representation type, aggregation depth, and learning configuration, with evaluations showing structured profiles outperform flat ones and aid generalization to new models.
Receipt and verification
| First computed | 2026-05-28T01:04:41.192497Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
89855f803e1b5fb3bdc4bed1a3bac8923744fcd402c085a3165d3a5354309920
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/RGCV7AB6DNP3HPOEX3I2HOWISI \
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Canonical record JSON
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