具有发散任务证据的稳定几何结构用于高效长时程智能体压缩

中文摘要 AI 辅助

长时程智能体会累积不断增长的交互历史，这增加了上下文和推理成本。我们发现，仅凭几何冗余不足以作为安全压缩的标准。尽管智能体历史表现出强烈的低维结构，但相似的全局几何结构可能保留非常不同的任务证据量。在保留块数相同的情况下，基于证据的选择将下一动作Top 3保留率从0.31提高到0.69，而质心相似度仍为0.98。受控替换进一步表明，与动作相关的信息可以被大幅改变，而全局几何度量几乎保持不变。受几何与证据之间这种差距的启发，我们引入了几何引导的证据保留记忆（GEM），这是一种无需训练的压缩器，它在使用几何残差完成覆盖之前，先保护任务和执行证据。GEM将每个任务的平均组合令牌使用量从2.69M降至2.11M，减少了21.4%，同时保持了相当的任务奖励。我们的结果表明，高效的智能体历史压缩应优化保留的任务证据，而非仅优化几何覆盖。

英文摘要

Long horizon agents accumulate growing interaction histories that increase context and inference costs. We find that geometric redundancy alone is an insufficient criterion for safe compression. Although agent histories exhibit strong low dimensional structure, similar global geometry can preserve very different amounts of task evidence. At identical retained block counts, evidence aware selection raises next action Top 3 retention from 0.31 to 0.69, while centroid similarity remains 0.98. Controlled replacement further shows that action related information can be substantially altered while global geometric measures remain nearly unchanged. Motivated by this gap between geometry and evidence, we introduce Geometry Guided Evidence Preserving Memory (GEM), a training free compressor that protects task and execution evidence before using geometric residuals to complete coverage. GEM reduces mean combined token usage from 2.69M to 2.11M per task, a 21.4% reduction, while maintaining comparable task reward. Our results show that efficient agent history compression should optimize for preserved task evidence rather than geometric coverage alone.

发表机构

• TierFlow Team（TierFlow团队）
• Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）
• Tsinghua University（清华大学）

机构由 AI 辅助整理，请以论文原文为准。