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cs.LG / cs.AI · 2608.11701 · 2026/08/12

Consolidator: Learning Persistent Routed Memory Across Context Boundaries

Goo, Sungwoo, Yun, Hwi-yeol, Jung, Sangkeun
TL;DR:短时记忆拷进慢速存储只是「持久化」,不保证影响后续访问。Consolidator是共享slot-local算子,把路由后的STM变换再累积进LTM,并喂回层次路由器以塑造后续输入访问哪些记忆槽。

🎯 问题

跨context boundary保留状态,但持久化本身不确保保留状态影响后续记忆访问——需要验证「学习式巩固」vs「强制恒等累积」的差别。

🔬 方法

Phasor Memory Network + Consolidator:两段式modulo-10映射任务,第二段在同一地址更新映射;冻结backbone与记忆接口,仅训练12.35K Consolidator参数(占29.95M模型的0.041%)。
章节结构(全文标题提取):
1 Introduction
2 Related Work
3 Problem Formulation and PMNet Background
4 Learned Latent-State Consolidation
5 Controlled Sequential Same-Address Update Task
6 Experimental Setup
7 Results
8 Discussion
2.1 Explicit and recurrent memory
2.2 Adaptive state and compact adaptation
2.3 Memory consolidation
3.1 Memory lifecycle
3.2 Hierarchical routed writes
3.3 Operational meaning of replay-free
4.1 Shared slot-local phase transform
4.2 Persistent accumulation and reset
4.3 Direct LTM-conditioned routing
4.4 Objectives
5.1 Procedural memory episodes
5.2 Two-stage training and evaluation protocol

📊 结果

直接LTM路由把更新映射召回从44.38%提升到87.02%(+42.64pp);学习式整合比强制恒等累积高21.40pp(无路由)/68.70pp(有路由)。巩固后的LTM既是被检索内容,也是塑造后续访问的访问状态。

💡 与研究方向关联

「持久化 ≠ 影响后续访问」——这个区分对 agent 记忆架构很重要。学习式巩固让 LTM 既当内容又当访问路由状态,是记忆系统设计的精微点。

📝 原文摘要

▶ 原文摘要 Abstract
Copying short-term memory (STM) into a slower store can preserve state across a context boundary, but persistence alone does not ensure that the retained state influences subsequent memory access. We test this distinction in a Phasor Memory Network (PMNet) using Consolidator, a shared slot-local operator that transforms routed STM before accumulating it into long-term memory (LTM), without replaying the source tokens. After each consolidation, the KV cache and STM are cleared. The retained LTM can still be read and is also fed into the hierarchical router, thereby conditioning which explicit-memory slots subsequent inputs access. We evaluate this mechanism on a two-segment modulo-10 mapping task in which the second segment updates the mapping at the same memory address. Following a second consolidation and reset, a held-out query must recover the updated mapping from LTM. The backbone and memory interface are frozen, leaving only 12.35K Consolidator parameters trainable (0.041\% of a 29.95M model). Across five paired runs from the same STM-pretraining checkpoint, direct LTM routing raises updated-mapping recall from $44.38\pm1.94\%$ to $87.02\pm1.76\%$ ($+42.64\pm1.10$ percentage points), while immediate STM recall remains 89.90\% in both conditions; both train separate Consolidators and retain the same LTM read paths. Learned consolidation outperforms forced identity accumulation by $21.40\pm1.91$ percentage points without routing and $68.70\pm1.76$ with routing. Thus, on this task, consolidated LTM serves as both retrievable content and an access state that shapes subsequent slot selection.
Deep Read · 2026-08-14高松灯 / Agent 日报
Memory ConsolidationPersistent MemoryContext BoundaryRouting