arXiv (All)
open access
The KV Cache Working Set: Online Capacity Planning for LLM Inference Systems
Prefix caching is critical for efficient large language model (LLM) serving, particularly for agentic workloads that repeatedly invoke the model with a growing conversation and tool-use history. By reusing the key-value (KV) states of previously processed prefixes, prefix caching avoids redundant pr
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
- ATTUNER: Recomputation-Free KV Cache Reuse via Query-Side Adaptation#1131923
- Working Around the Compute Ceiling: Byte-Exact Memory in Galahad Makes LLM Reading a One-Time Cost LLM Reading a One-Time Cost#1144325
- Distilling Sequential Computation in Transformer Language Models#1063444
- PDEU-Bench: Benchmarking the Personalized Planning Lifecycle of Tool-Calling LLM Agents#1129540
- Large Language Models as Optimization Controllers: Adaptive Continuation for SIMP Topology Optimization#1049781
Record · ID 1064290
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