Problem
Research questions and friction points this paper is trying to address.
long-term memory
memory extraction
agent systems
memory pollution
structured memory
MemReader: From Passive to Active Extraction for Long-Term Agent Memory
🤖 AI Summary
Current approaches to long-term memory construction in intelligent agents rely on passive, one-shot information extraction, which is highly susceptible to noisy dialogue, missing references, and cross-turn dependencies, often resulting in memory contamination and inconsistency. This work proposes the MemReader family of models, introducing for the first time an active memory extraction paradigm wherein agents dynamically evaluate the value, referential clarity, and completeness of incoming information to autonomously decide whether to write, delay, retrieve, or discard it. Through model distillation, we develop MemReader-0.6B and further optimize MemReader-4B using Group Relative Policy Optimization (GRPO), enabling reasoning-driven memory management within the ReAct framework. Evaluated on LOCOMO, LongMemEval, and HaluMem benchmarks, MemReader-4B achieves state-of-the-art performance in knowledge updating, temporal reasoning, and hallucination suppression, and has been integrated into MemOS for real-world deployment.
📝 Abstract
Long-term memory is fundamental for personalized and autonomous agents, yet populating it remains a bottleneck. Existing systems treat memory extraction as a one-shot, passive transcription from context to structured entries, which struggles with noisy dialogue, missing references, and cross-turn dependencies, leading to memory pollution, low-value writes, and inconsistency. In this paper, we introduce the MemReader family for active long-term memory extraction in agent systems: MemReader-0.6B, a compact and cost-efficient passive extractor distilled for accurate and schema-consistent structured outputs, and MemReader-4B, an active extractor optimized with Group Relative Policy Optimization (GRPO) to make memory writing decisions. Under a ReAct-style paradigm, MemReader-4B explicitly evaluates information value, reference ambiguity, and completeness before acting, and can selectively write memories, defer incomplete inputs, retrieve historical context, or discard irrelevant chatter. Experiments on LOCOMO, LongMemEval, and HaluMem show that MemReader consistently outperforms existing extraction-based baselines. In particular, MemReader-4B achieves state-of-the-art performance on tasks involving knowledge updating, temporal reasoning, and hallucination reduction. These results suggest that effective agent memory requires not merely extracting more information, but performing reasoning-driven and selective memory extraction to build low-noise and dynamically evolving long-term memory. Furthermore, MemReader has been integrated into MemOS and is being deployed in real-world applications. To support future research and adoption, we release the models and provide public API access.
Innovation
Methods, ideas, or system contributions that make the work stand out.
active memory extraction
Group Relative Policy Optimization
ReAct paradigm
selective memory writing
long-term agent memory
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