arXiv — cs.AI preprintsInternational16 October 2026
DropKV: Decoupling Residual-Output Perturbation for Near-Optimal KV-Cache Eviction
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.22130v1 Announce Type: new Abstract: Key-value (KV) cache management has become a critical bottleneck for scaling Large Language Models (LLMs) to long-context scenarios due to linear memory growth. While recent eviction methods have moved beyond simple attention-score heuristics to incorporate value representations, they typically face a fundamental trade-off: the underlying optimization for minimizing output perturbation is an NP-hard combinatorial problem, leading to mathematically suboptimal heuristics or implementations that are difficult to kernelize. In this work, we propose D
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock16 October 2026
- Evaluating Model Capabilities After Benchmark Saturation16 October 2026
- Value-Informed Schedule Trimming for Accelerated Sequential Monte Carlo Guidance in Discrete Diffusion16 October 2026
- AlphaDiana: A Measurement System for Agents in Verifiable Reasoning Tasks16 October 2026
- When Correctness Feedback Erases Evidence: Phase-Separated Completeness Auditing in Verus16 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.