SKILL.state cuts hundred-step agent tokens to about one-sixteenth
On a hundred-step warehouse task, accuracy is 0.94 and token use is about one-sixteenth of keeping full history, but it relies on predefined sufficient patterns and is not independently reproduced.
SKILL.state cuts token use on a hundred-step warehouse task to about one-sixteenth of keeping the full history, while still reaching 0.94 accuracy; public agent benchmarks also show lower consumption.
Previously agents typically kept the full history, so tokens grew linearly with steps. SKILL.state compresses history into an explicit state, supporting the "state-first" engineering hypothesis, but not enough to prove general agent reliability.
The result is reported in an arXiv preprint, and the method relies on predefined sufficient patterns.
It does not yet cover multi-agent concurrent writes, and it has not been independently reproduced; next steps require code, reproduction and production tasks.
Source check Full paper and limitations ↗
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