CR^2 matches full-information routing performance for device-edge LLM inference using only device-side signals and cuts normalized deployment cost by up to 16.9% at matched accuracy.
LLMRank: Understanding LLM strengths for model routing
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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citation-polarity summary
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2026 3verdicts
UNVERDICTED 3representative citing papers
The Capability Frontier shows standard LLM benchmarks underestimate performance by 82% by ignoring model specialization across questions and the benefits of multiple generations per query.
Marketplace Evaluation uses repeated-interaction simulations to assess information access systems with marketplace-level metrics such as retention and market share that complement traditional accuracy measures.
citing papers explorer
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CR^2: Cost-Aware Risk-Controlled Routing for Wireless Device-Edge LLM Inference
CR^2 matches full-information routing performance for device-edge LLM inference using only device-side signals and cuts normalized deployment cost by up to 16.9% at matched accuracy.
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The Capability Frontier: Benchmarks Miss 82% of Model Performance
The Capability Frontier shows standard LLM benchmarks underestimate performance by 82% by ignoring model specialization across questions and the benefits of multiple generations per query.
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Evaluation of Agents under Simulated AI Marketplace Dynamics
Marketplace Evaluation uses repeated-interaction simulations to assess information access systems with marketplace-level metrics such as retention and market share that complement traditional accuracy measures.