Ensemble Routing for Prompt-Adaptive Cost-Aware LLM Inference
arXiv:2611.10193v1 Announce Type: new Abstract: Large language models (LLMs) increasingly support Web-based services that must balance response quality and inference cost across heterogeneous requests. Conventional routing selects one model per prompt, while fixed ensembles cannot adapt their composition and cost…
Excerpt provided by arXiv cs.AI via its public feed. Full reporting belongs to the original publisher.
Read the full story on arXiv cs.AI
Related coverage
The Decoder · 34m ago
Gemini 4 Carbon arrives just in time to feel like yesterdays news in the fastest model cycle yet
Ars Technica AI · 3h ago
DeepMind reveals Gemini Robotics 2.5 and open safety benchmark "ASIMOV-jailbreak"
Google DeepMind · 6h ago
Introducing AlphaProof Code: a breakthrough in verifying complex software at depth
OpenAI · 9h ago
Closing the AI guidance gap in education