arXiv (All)
open access
BELT : BEhavior-Aligned Low-Rank KV Cache Compression with Learned Top-k Rank Allocation
Long-context inference makes the key-value (KV) cache a major memory bottleneck for large language models. We propose BELT, a low-rank KV cache compression framework. Its key-side objective targets attention distributions rather than raw keys or pre-softmax logits, yielding a quadratic surrogate wit
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
- DropKV: Decoupling Residual-Output Perturbation for Near-Optimal KV-Cache Eviction#1357785
- EpiKV: Epiphany-Aware KV Cache Eviction Without the Attention Matrix#1118621
- Draft-KV: Learning Useful Latent Communication Between Language Models#1129012
- AI Knows When It's Being Watched: Functional Strategic Action and Contextual Register Modulation in Large Language Models#187357
- Block Sparse Attention with Log-Linear Complexity#1105651
Record · ID 1343165
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