R$^3$Mem: Bridging Memory Retention and Retrieval via Reversible Compression
This addresses memory inefficiencies in LLMs for real-world applications like conversational agents, though it appears incremental as it builds on existing memory designs.
The paper tackles the problem of memory retention and retrieval in LLMs by proposing R$^3$Mem, a memory network using reversible compression, which achieves state-of-the-art performance in long-context language modeling and retrieval-augmented generation tasks.
Memory plays a key role in enhancing LLMs' performance when deployed to real-world applications. Existing solutions face trade-offs: explicit memory designs based on external storage require complex management and incur storage overhead, while implicit memory designs that store information via parameters struggle with reliable retrieval. In this paper, we propose R$^3$Mem, a memory network that optimizes both information Retention and Retrieval through Reversible context compression. Specifically, R$^3$Mem employs virtual memory tokens to compress and encode infinitely long histories, further enhanced by a hierarchical compression strategy that refines information from document- to entity-level for improved assimilation across granularities. For retrieval, R$^3$Mem employs a reversible architecture, reconstructing raw data by invoking the model backward with compressed information. Implemented via parameter-efficient fine-tuning, it can integrate seamlessly with any Transformer-based model. Experiments demonstrate that our memory design achieves state-of-the-art performance in long-context language modeling and retrieval-augmented generation tasks. It also significantly outperforms conventional memory modules in long-horizon interaction tasks like conversational agents, showcasing its potential for next-generation retrieval systems.