MemForest: An Efficient Agent Memory System with Hierarchical Temporal Indexing

• URL: http://arxiv.org/abs/2605.23986v1
• Date: Sat, 16 May 2026 13:11:47 GMT
• Title: MemForest: An Efficient Agent Memory System with Hierarchical Temporal Indexing
• Abstract summary: We present MemForest, a memory framework that reformulates agent memory as a write-efficient temporal data management problem.<n>MemForest breaks the sequential bottleneck via parallel chunk extraction, decoupling memory construction into concurrent, independent operations.<n>We evaluate MemForest on two long-context memory benchmarks, LongMemEval-S and LoCoMo.
• Score: 33.94614583313314
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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