# Self-Compact：让语言模型智能体自行决定何时压缩轨迹

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-22 08:00
- AIHOT 分数：61
- AIHOT 链接：https://aihot.virxact.com/items/cmqq6uzx6075fslp5v9h3malz
- 原文链接：https://arxiv.org/abs/2606.23525

## AI 摘要

长期agent轨迹会积累陈旧内容，最终超出上下文窗口。现有固定token阈值压缩忽略轨迹结构，可能丢失中间结果。SelfCompact提供压缩工具供模型调用，并配套轻量级规则指明触发时机（子任务完成或轨迹收敛）与抑制时机（中途推导或卡住），实现自适应压缩，无需微调或外部监督。在六个基准及七种模型上，SelfCompact以远低于固定间隔压缩的token成本达到相近或更优效果：数学相比无压缩基线最高提升18.1分，智能体搜索提升5–9分，每题成本降低30–70%。

## 正文

Long agent traces composed of chains of thought and tool calls accumulate stale content that anchor subsequent generations, and eventually outgrow the context window. Existing scaffolds mitigate it with fixed-interval compaction triggered at a token threshold. Such triggers pay no heed to trajectory structure, risking discard of partial results mid-derivation or mid-search. We propose SelfCompact, a scaffold that allows the model itself to decide when and how to compact. Specifically, it pairs two inference-time elements: (i) a compaction tool the model invokes to summarize the accumulated context, and (ii) a lightweight rubric specifying when to fire (a sub-task has resolved, or the trajectory is converging) and when to suppress (mid-derivation, or when stuck). Both are needed. The tool alone is unevenly used across open-weight models, often invoked at unhelpful moments or not at all; the rubric alone cannot act. Together, they elicit effective adaptive compaction without any fine-tuning or external supervision. We present empirical results on six benchmarks (competitive math and agentic search) and seven models. Our results show that SelfCompact matches or exceeds fixed-interval summarization at a fraction of the token cost, improving over a no-summarization baseline by up to 18.1 points on math and 5-9 points on agentic search at 30-70% lower per-question cost. Our results expose a meta-cognitive gap: although unprompted models cannot reliably tell when their own context is rotting, a lightweight rubric closes this gap, reframing when to compact as a capability that scaffolds can supply without training.