arXiv: cs.AI preprintsInternational5 November 2026
SUMONE: A Simple and Efficient Summarizer for Plug-and-Play Agent Memory
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arXiv:2611.06445v1 Announce Type: new Abstract: Long-horizon language-model agents accumulate increasingly large interaction histories, making context compaction essential for efficient execution. Existing learned compaction methods often rely on downstream task outcomes or solver behavior, coupling training to specific tasks or agents. We introduce SumOne, a standalone summarizer trained to preserve information in agent histories through masked reconstruction. Given a summary of the expired history, a frozen reconstructor recovers randomly masked portions of the original trajectory; reconstru
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