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S$^4$R: Selective Sampling, Subspaces, and Sparse Reconstruction for Compressed Long-Context KV Caching
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Researchers propose S4R, a novel method to compress LLM Key-Value caches for long-context windows without high compute or calibration overhead.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 23 September 2026
- Collected by OneBench
- 4 Aug 2026, 13:23 UK
Stored source excerpt
arXiv:2608.00528v1 Announce Type: new Abstract: The growth of context window lengths in Large Language Models (LLMs) significantly enhances their long-context capabilities but incurs prohibitive memory…
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