arXiv (All)
open access
Drift-Aware LLM Routing with Sparse Contexts and Shared Budgets
A multi-model language service must route each request while preserving workload-level budgets for compute, latency, memory, or monetary cost. Two features make this problem materially harder than static model selection. Prompt representations are high dimensional, so only a small subset of embeddin
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- LLM+P: Empowering Large Language Models with Optimal Planning Proficiency#16624
- You're Hired: Strategic Model Selection for LLM Collaboration#1143417
- Trajectory Soup: Pushing the Compute-Scaling Frontier of LLM Mid-training via Diverse Trajectories#1132370
- NetLLM: Adapting Large Language Models for Networking#328716
- To Each Language Its Tokenizer: Modular Tokenizers for Efficient Multilingual LLMs#1109526
Record · ID 820084
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive