arXiv CS
open access
SARA: SLO-Aware Resource Allocation for Disaggregated Agentic LLM Services
Recent advances in large language models (LLMs) are driving the emergence of multi-modal and agentic services for mobile users through cloud and edge infrastructures, where long-context workloads pose daunting challenges for inference latency. Existing disaggregated LLM serving systems largely rely
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
- SARA: SLO-Aware Resource Allocation for Disaggregated Agentic LLM Services#1062688
- KAIROS: Stateful, Context-Aware Power-Efficient Agentic Inference Serving#120489
- Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks#394448
- Detecting Hidden Behaviors in LLMs via Activation-matched Finetuning#819803
- Detecting Hidden Behaviors in LLMs via Activation-matched Finetuning#809807
Record · ID 1043063
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