PlugMem: A Task-Agnostic Plugin Memory Module for LLM Agents
arXiv:2603.03296v1 Announce Type: cross Abstract: Long-term memory is essential for large language model (LLM) agents operating in complex environments, yet existing memory designs are either task-specific and non-transferable, or task-agnostic but less effective due to low task-relevance and context explosion from raw memory retrieval. We propose PlugMem, a task-agnostic plugin memory module that can be attached to arbitrary LLM agents without task-specific redesign. Motivated by the fact that decision-relevant information is concentrated as abstract knowledge rather than raw experience, we draw on cognitive science to structure episodic memories into a compact, extensible knowledge-centric memory graph that explicitly represents propositional and prescriptive knowledge. This representation enables efficient memory retrieval and reasoning over task-relevant knowledge, rather than verbose raw trajectories, and departs from other graph-based methods like GraphRAG by treating knowledge
arXiv:2603.03296v1 Announce Type: cross Abstract: Long-term memory is essential for large language model (LLM) agents operating in complex environments, yet existing memory designs are either task-specific and non-transferable, or task-agnostic but less effective due to low task-relevance and context explosion from raw memory retrieval. We propose PlugMem, a task-agnostic plugin memory module that can be attached to arbitrary LLM agents without task-specific redesign. Motivated by the fact that decision-relevant information is concentrated as abstract knowledge rather than raw experience, we draw on cognitive science to structure episodic memories into a compact, extensible knowledge-centric memory graph that explicitly represents propositional and prescriptive knowledge. This representation enables efficient memory retrieval and reasoning over task-relevant knowledge, rather than verbose raw trajectories, and departs from other graph-based methods like GraphRAG by treating knowledge as the unit of memory access and organization instead of entities or text chunks. We evaluate PlugMem unchanged across three heterogeneous benchmarks (long-horizon conversational question answering, multi-hop knowledge retrieval, and web agent tasks). The results show that PlugMem consistently outperforms task-agnostic baselines and exceeds task-specific memory designs, while also achieving the highest information density under a unified information-theoretic analysis. Code and data are available at https://github.com/TIMAN-group/PlugMem.
Executive Summary
This article introduces PlugMem, a task-agnostic plugin memory module designed for large language model (LLM) agents. By structuring episodic memories into a compact, extensible knowledge-centric memory graph, PlugMem enables efficient memory retrieval and reasoning over task-relevant knowledge. The authors evaluate PlugMem across three heterogeneous benchmarks and demonstrate its superiority over task-agnostic baselines and task-specific memory designs. PlugMem's ability to achieve high information density under a unified information-theoretic analysis further solidifies its effectiveness. The proposed approach has significant implications for the development of more efficient and effective LLM agents, particularly in complex environments where long-term memory is crucial. Code and data are available for further research and exploration.
Key Points
- ▸ PlugMem is a task-agnostic plugin memory module designed for LLM agents.
- ▸ The module structures episodic memories into a compact, extensible knowledge-centric memory graph.
- ▸ PlugMem outperforms task-agnostic baselines and task-specific memory designs across three heterogeneous benchmarks.
Merits
Strength in Task-Agnosticity
PlugMem's task-agnostic design allows it to be easily attached to arbitrary LLM agents without requiring task-specific redesign, making it a versatile and efficient solution for a wide range of applications.
Effective Knowledge Retrieval and Reasoning
The proposed approach enables efficient memory retrieval and reasoning over task-relevant knowledge, departing from other graph-based methods by treating knowledge as the unit of memory access and organization.
Demerits
Potential Overhead in Memory Usage
Structuring episodic memories into a compact, extensible knowledge-centric memory graph may require significant computational resources and memory usage, potentially leading to overhead in real-world applications.
Limited Generalizability to Non-Knowledge-Based Tasks
The proposed approach is specifically designed for knowledge-based tasks, and its generalizability to non-knowledge-based tasks, such as those requiring raw experience or sensorimotor interactions, is unclear.
Expert Commentary
The proposed approach of structuring episodic memories into a compact, extensible knowledge-centric memory graph is a significant contribution to the field of LLM agents. By enabling efficient memory retrieval and reasoning over task-relevant knowledge, PlugMem has the potential to revolutionize the development of LLM agents. However, further research is needed to address potential limitations and overheads in memory usage. The proposed approach also highlights the need for further exploration of graph-based methods in memory representation and task-agnostic memory designs for LLM agents. As the field continues to evolve, it is essential to consider the implications of PlugMem for real-world applications and policy-making.
Recommendations
- ✓ Further research should focus on addressing potential limitations and overheads in memory usage, such as computational resources and memory usage.
- ✓ Exploration of graph-based methods in memory representation and task-agnostic memory designs for LLM agents should continue to be a pressing research issue.