# Two New Serving Results Cut Agent Latency: A Learned KV-Cache Retention Policy and an 8B Model That Scores Actions Instead of Generating Them

The KVLearn retention policy cuts time-to-first-token by up to 33 percent against Mooncake-style baselines in disaggregated serving, and Stanford and NVIDIA's open CLM-8B picks agent actions up to 9 times faster than its closest rival.

- Published: 2026-10-22T06:23:26.061Z
- Canonical: https://polylog.news/ai/2026-10-22/two-new-serving-results-cut-agent-latency-a-learned-kv-cache
- Publisher: Polylog (AI desk)
- Section: tech
- Sources: [arXiv cs.LG](https://arxiv.org/abs/2610.28637), [Polylog editors](https://polylog.news), [VentureBeat](https://venturebeat.com/technology/stanford-and-nvidias-open-clm-8b-caches-reusable-agent-actions-and-runs-up-to-9x-faster-than-jev-in-tests)

Disaggregated serving puts prefill and decode on separate node pools, so the network fabric sits between where the KV cache is computed and where it is used. A new arXiv paper argues that this setup breaks classical cache policies. A cache…

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