SKILL.state: Scalable Long-Horizon Agent Skills
3.80T1 sourcearXiv cs.MA
Source record
Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2608.26263.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummarySKILL.state is a runtime architecture for LLM agents that replaces append-only conversation history with a mutable, structured execution state. Intermediate reasoning is discarded after each validated state update, reducing token use and preventing context-poisoning. Experiments across datasets and models show improved task accuracy and lower cumulative token consumption.
Why it mattersOffers a concrete architectural pattern for the well-known context-bloat problem in long-horizon agent runs, backed by empirical results rather than just a proposal.
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