SumONE：面向即插即用智能体记忆的简单高效摘要器
SUMONE: A Simple and Efficient Summarizer for Plug-and-Play Agent Memory
AI 导读
arXiv 论文提出 SumONE，一个独立的智能体历史摘要器，通过掩码重构与 GRPO 训练，无需参考摘要或下游任务执行即可实现自监督学习。推理时仅保留轻量摘要器，可即插即用地为不变的智能体压缩上下文。
正文 · 原文
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; reconstruction quality, combined with a length penalty, provides a task-agnostic reward for optimizing the summarizer with GRPO. This yields self-supervision directly from raw trajectories, without reference summaries or downstream task execution. At inference time, only the lightweight summarizer is retained, enabling plug-and-play context compression for otherwise unchanged agents. We conduct a systematic evaluation of SumOne in long-horizon search and software-engineering tasks. SumOne reduces estimated inference cost by approximately 78\% on DeepSearchQA and 75\% on SWE-bench Verified relative to operating with full context, with absolute decreases of 0.04. The same checkpoint also transfers across downstream models and agent harnesses without additional fine-tuning.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.06445 [cs.AI] |
| (or arXiv:2611.06445v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06445 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jizhe Zhang [view email]
[v1]
Wed, 4 Nov 2026 12:49:07 UTC (1,488 KB)
来源：arXiv cs.AI · arxiv.org