MemForest: An Efficient Agent Memory System with Hierarchical Temporal Indexing

Han Chen; Zining Zhang; Wenqi Pei; Bingsheng He; Ming Wu; Jason Zeng; Michael Heinrich; Wei Wu; Hongbao Zhang

title	MemForest: An Efficient Agent Memory System with Hierarchical Temporal Indexing
authors	Han Chen, Zining Zhang, Wenqi Pei, Bingsheng He, Ming Wu, Jason Zeng, Michael Heinrich, Wei Wu, Hongbao Zhang
date	2026-05-16
venue	arXiv (preprint, not peer reviewed)
id	arXiv:2605.23986
upvotes	15 (Hugging Face Daily Papers, 2026-11-01)
links	abs pdf hf code
primer	Memory for AI agents
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.

cite

@misc{chen2026memforest,
  title={MemForest: An Efficient Agent Memory System with Hierarchical Temporal Indexing},
  author={Han Chen and Zining Zhang and Wenqi Pei and Bingsheng He and Ming Wu and Jason Zeng and Michael Heinrich and Wei Wu and Hongbao Zhang},
  year={2026},
  eprint={2605.23986},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2605.23986}
}