Chain-of-Memory: Lightweight Memory for LLM Agents

The Chain-of-Memory (CoM) framework presents a novel approach to external memory systems for Large Language Model (LLM) agents. By advocating for lightweight memory construction paired with sophisticated utilization, CoM addresses the limitations of existing paradigms that rely on computationally expensive memory construction and simple context concatenation. The Chain-of-Memory mechanism organizes retrieved fragments into coherent inference paths through dynamic evolution, utilizing adaptive truncation to prune irrelevant noise. The empirical results demonstrate that CoM outperforms strong baselines with significant accuracy gains while drastically reducing computational overhead. This suggests that complex memory architectures may not always be necessary for achieving high performance in LLM agents. The reduced computational overhead and improved accuracy of CoM could have significant implications for the development of more efficient and effective LLM agents, enabling them to perform long-horizon decision-making more effectively. Further research is needed to explore the limitations of CoM and validate its performance in real-world scenarios.

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