用分层记忆解决智能体摘要丢失证据的问题，省钱省时还可信。
From Lossy to Verified: A Provenance-Aware Tiered Memory for Agents
- 分两层内存：先查快摘要，不够再查原始日志
- 在LoCoMo数据集上准确率0.851，输入令牌减少54.1%
- 适合需要可验证推理的长周期智能体系统
长周期智能体常将交互历史压缩为写入时的摘要，导致‘先写后查’困境：压缩决策在查询前做出，可能遗漏关键信息（如过敏史），使答案无法追溯证据。保留原始日志虽能保证可验证性，但默认使用会显著增加上下文长度，导致令牌消耗和延迟上升。我们提出TierMem，一种与溯源关联的分层内存框架，将检索视为推理时的证据分配问题。该框架采用两级内存结构：默认查询快速摘要索引，当摘要证据不足时，运行时路由器自动升级至不可篡改的原始日志存储。随后，验证结果作为新摘要单元回写，并关联原始来源。在LoCoMo数据集上，TierMem达到0.851的准确率（相比纯原始日志的0.873），输入令牌减少54.1%，延迟降低60.7%。
原文摘要 · Abstract (English)
Long-horizon agents often compress interaction histories into write-time summaries. This creates a fundamental write-before-query barrier: compression decisions are made before the system knows what a future query will hinge on. As a result, summaries can cause unverifiable omissions -- decisive constraints (e.g., allergies) may be dropped, leaving the agent unable to justify an answer with traceable evidence. Retaining raw logs restores an authoritative source of truth, but grounding on raw logs by default is expensive: many queries are answerable from summaries, yet raw grounding still requires processing far longer contexts, inflating token consumption and latency. We propose TierMem, a provenance-linked framework that casts retrieval as an inference-time evidence allocation problem. TierMem uses a two-tier memory hierarchy to answer with the cheapest sufficient evidence: it queries a fast summary index by default, and a runtime sufficiency router Escalates to an immutable raw-log store only when summary evidence is insufficient. TierMem then writes back verified findings as new summary units linked to their raw sources. On LoCoMo, TierMem achieves 0.851 accuracy (vs.0.873 raw-only) while reducing input tokens by 54.1\% and latency by 60.7%.
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