arXiv (All)
open access
KVFetch: Temporal Prefetching for the Missing Half of KV Cache Compression
As context windows scale to tens or hundreds of thousands of tokens, KV cache compression has become essential for efficient LLM inference. Existing methods fall into three families: score-based eviction, summary compensation, and offload-and-recall. Yet all three decide what to keep or recall by co
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
- TreeSpark: Learning to Draft the Trees You Verify for Speculative Decoding#1355107
- When to Evict, Not What to Keep: Draft-Guided Eviction for Training-Free KV-Cache Compression#1118046
- Read What Matters: Query-Adaptive Quantization for KV Caches#1248172
- LongSpark: Efficient speculative decoding with a fixed-cost parallel drafter#1132230
- ChampionKV: Head-Wise Adaptive Budget Allocation for KV Cache Compression#1343223
Record · ID 1232850
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