论文提出用16个记忆token实现LLM智能体记忆
一篇论文提出用16个记忆token替代全上下文推理作为LLM的智能体记忆：文档一次性编码为可复用嵌入，查询时由学习模块从全集收集相关信息并写入冻结LLM输入空间的16个token。
// Latent Semantic Memory for LLMs //
This paper presents some really interesting findings about agent memory.
They find that sixteen memory tokens beat full-context inference on FactKG while using 108x fewer tokens for the source documents.
Approach:
Documents are encoded once into reusable embeddings. For each query, a learned module gathers the relevant information from the whole collection and writes it into 16 tokens in the input space of a frozen LLM.
Results:
It beats Mem0, MemoRAG, and other baselines on five of eight benchmarks. On FactKG, it scores 84.33% against 80.43% with the full context.
It also works as agent memory. In TextWorld, using the agent's own history as the collection raises success from 18.6% to 24.2%, and the agent revisits fewer rooms.
It also seems that it could help frozen LLMs accumulate and use knowledge effectively across long-context interactions. This is applicable to building continually learning AI systems.
arxiv.org/abs/2610.21375
Chat with Paper: academy.dair.ai/papers/memor…
来源：omarsar0 · x.com