SKILL.state: Scalable Long-Horizon Agent Skills

Abstract

Large Language Models (LLMs) increasingly act as autonomous agents executing complex, long-running procedural skills. Existing agent runtimes maintain execution by continually appending observations, actions, and intermediate reasoning traces to an ever-growing conversation history, causing latency degradation and context-poisoning failures over long horizons. We present SKILL.state, a runtime architecture that replaces append-only conversational history with an explicit, mutable execution state

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@misc{badhe2026skillstate,
  title = {{SKILL.state: Scalable Long-Horizon Agent Skills}},
  author = {Sanket Badhe and Priyanka Tiwari and Jonghyun Chung},
  year = {2026},
  month = aug,
  eprint = {2608.26263},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2608.26263}
}