arXiv — cs.AI preprintsInternational15 October 2026
Budgeted KV-Cache Repair for Evolving Contexts
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:2610.17338v1 Announce Type: new Abstract: Language-model agents revise their working context as information becomes obsolete, but deleting text does not erase its influence on cached computation. Later key-value (KV) states remain conditioned on the deleted content, while recomputing the entire retained suffix can be costly. We introduce budgeted KV-cache repair: recovering task performance by recomputing a user-chosen fraction of the retained suffix. Comparing repair allocations under identical relevance scores and token budgets, we find that refreshing coherent regions can substantiall
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- Artificiality's First Exam: An LLM-Referenced C-test for Supraintelligence15 October 2026
- The Silent Failures in Agentic Data Science15 October 2026
- ClawLoop: Self-Improving Agents via Harness-Native Training Loop15 October 2026
- Correction Training for Diffusion Models15 October 2026
- Long-Horizon-Terminal-Bench 2.0: Automating Diverse Task Construction and Measuring Sustained Agent Progress15 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.