Abstract:Memory is a fundamental component for enabling long-context LLM agents, supporting persistent state across interactions through a continuous serve-and-update lifecycle. Despite substantial prior work, existing systems suffer from significant maintenance overhead due to two key limitations: coarse-grained state management and inherently sequential update pipelines. In particular, updates are often tightly coupled with LLM inference and require full-state rewrites, leading to poor scalability and growing latency as memory accumulates. To address these challenges, we present MemForest, a memory framework that reformulates agent memory as a write-efficient temporal data management problem. MemForest breaks the sequential bottleneck via parallel chunk extraction, decoupling memory construction into concurrent, independent operations. To further eliminate coarse-grained maintenance, we introduce MemTree, a hierarchical temporal index that organizes memory as time-ordered trees rather than flat global summaries. This design replaces full-state rewrites with localized per-node updates, reducing maintenance cost to the affected tree paths while naturally preserving temporally evolving states. We evaluate MemForest on two long-context memory benchmarks, LongMemEval-S and LoCoMo. On LongMemEval-S, MemForest achieves the best overall performance among stateful baselines, reaching 79.8% pass@1 accuracy while sustaining a memory construction throughput approximately 6x higher than state-of-the-art approaches including EverMemOS.
| Comments: | 12 pages. Extended version with appendix as supplemental material. Submitted to VLDB |
| Subjects: | Databases (cs.DB); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) |
| ACM classes: | H.2.4; I.2.7; I.2.11 |
| Cite as: | arXiv:2605.23986 [cs.DB] |
| (or arXiv:2605.23986v1 [cs.DB] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23986 arXiv-issued DOI via DataCite |
From: Han Chen [view email]
[v1]
Sat, 16 May 2026 13:11:47 UTC (549 KB)
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