NextMem: Towards Latent Factual Memory for LLM-based Agents
arXiv:2603.15634v1 Announce Type: new Abstract: Memory is critical for LLM-based agents to preserve past observations for future decision-making, where factual memory serves as its foundational part. However, existing approaches to constructing factual memory face several limitations. Textual methods impose heavy context and indexing burdens, while parametric methods suffer from catastrophic forgetting and high costs. To address these challenges, we introduce NextMem, a latent factual memory framework that utilizes an autoregressive autoencoder to efficiently construct latent memory while ensuring accurate reconstruction. For better optimization, we propose a two-stage training process, including autoregressive reconstruction alignment and progressive latent substitution. We also incorporate quantization to reduce storage overhead. Extensive experiments demonstrate that NextMem achieves superior performance, and excels in retrieval, robustness, and extensibility properties. We release
arXiv:2603.15634v1 Announce Type: new Abstract: Memory is critical for LLM-based agents to preserve past observations for future decision-making, where factual memory serves as its foundational part. However, existing approaches to constructing factual memory face several limitations. Textual methods impose heavy context and indexing burdens, while parametric methods suffer from catastrophic forgetting and high costs. To address these challenges, we introduce NextMem, a latent factual memory framework that utilizes an autoregressive autoencoder to efficiently construct latent memory while ensuring accurate reconstruction. For better optimization, we propose a two-stage training process, including autoregressive reconstruction alignment and progressive latent substitution. We also incorporate quantization to reduce storage overhead. Extensive experiments demonstrate that NextMem achieves superior performance, and excels in retrieval, robustness, and extensibility properties. We release our code and model checkpoints at https://github.com/nuster1128/NextMem.
Executive Summary
The article introduces NextMem, a latent factual memory framework for LLM-based agents. NextMem utilizes an autoregressive autoencoder to efficiently construct latent memory, ensuring accurate reconstruction. The framework addresses limitations of existing approaches, including heavy context and indexing burdens, catastrophic forgetting, and high costs. A two-stage training process and quantization are proposed to optimize performance and reduce storage overhead. Extensive experiments demonstrate NextMem's superior performance in retrieval, robustness, and extensibility properties.
Key Points
- ▸ NextMem is a latent factual memory framework for LLM-based agents
- ▸ Autoregressive autoencoder is used for efficient latent memory construction
- ▸ Two-stage training process and quantization are proposed for optimization
Merits
Efficient Memory Construction
NextMem's autoregressive autoencoder enables efficient construction of latent memory, reducing computational costs and improving performance.
Demerits
Complexity of Autoregressive Autoencoder
The use of an autoregressive autoencoder may add complexity to the framework, potentially requiring significant computational resources and expertise.
Expert Commentary
NextMem represents a significant advancement in the development of latent factual memory frameworks for LLM-based agents. The use of an autoregressive autoencoder and two-stage training process demonstrates a nuanced understanding of the challenges associated with existing approaches. However, further research is needed to fully explore the potential applications and limitations of NextMem. The release of the code and model checkpoints is a welcome move, enabling the research community to build upon and extend this work.
Recommendations
- ✓ Further research is needed to explore the potential applications of NextMem in various domains
- ✓ The development of NextMem should be accompanied by a thorough evaluation of its potential impact on data storage and privacy policies