SimpleMem: Efficient Lifelong Memory for LLM Agents
- Published
- Source
- arXiv
- Paper number
- 111
- Field
- Memory / Agents
- arXiv ID
- 2601.02553
Key points
- Large language models (LLMs) struggle with fixed context windows, which limits their ability to retain information across long-term memory and extended interactions.
- Memory systems for LLM agents suffer from context bloat caused by redundant and low-entropy conversations, which reduces effective information density and model performance.
- Current approaches to augmenting LLM memory incur substantial computational overhead, including high token costs and increased latency, which hinders practical deployment.
- The framework features a three-stage memory pipeline that includes semantic structured compression, multidimensional structured indexing with recursive consolidation, and adaptive query-aware retrieval.
- It implements entropy-aware filtering to remove redundant conversations and normalize informative content into self-contained, context-independent memory units.
- It uses multilayer indexing, semantic, lexical, and symbolic, along with an asynchronous background process for recursive consolidation, to produce higher-level abstract memory representations.
Paper links
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