Retrieval as Traversal：用地址空间遍历替代全局检索的有状态记忆方法
Retrieval as Traversal: Value-Indexed Memory for Stateful Long-Context Agents
一篇记忆机制的论文，把检索改成局部遍历，在 MiniGrid Memory 上用五 token 上下文就跑出 0.95 回报，训练步数比绝对编址少好几倍。
论文提出把检索建模为在地址空间中的持久局部遍历，将记忆的内容与地址分离，用成本正则化的索引目标衡量信息价值与检索开销。在 MiniGrid Memory 任务上，仅用五 token 上下文和固定几何的遍历头，0.59M 训练步就达到 0.95 平均回报，而 Gaussian 绝对编址需 0.94M 步、categorical 绝对编址超过 2M 步。该方法让互补记忆因边际价值超过检索成本而被共同检索，冗余记忆则被排除。作者指出实验只验证了最简单的固定几何实例，学习几何和重复检索尚未测试。
Retrieval as Traversal: Value-Indexed Memory for Stateful Long-Context Agents
Long-context models repeatedly solve closely related retrieval problems. Attention or global retrieval can retain large stores, but each forward pass again searches for what matters. Recurrent compression avoids this search only by deciding earlier what information to preserve. Neither mechanism directly exploits that useful memories at neighbouring retrieval steps may substantially overlap. We propose an alternative in which retrieval is a persistent local traversal through an address space. We separate a memory's content from its address. Utility is defined by the information revealed by retrieving the content, while geometry determines only how cheaply that information can be reached. This leads to a cost-regularised indexing objective that preserves information value inside local neighbourhoods while charging for every memory made locally accessible. Its marginal-value form gives both attractive and exclusion pressures. Thus, complementary memories should be co-retrievable when the additional value exceeds retrieval cost, whereas redundant memories should not occupy the same retrieval set solely because they are individually useful. As a proof of principle, we test only the simplest fixed-geometry instance on MiniGrid Memory. A persistent local traversal head reaches $0.95$ mean return after $0.59$M training steps with a five-token context, compared with $0.94$M for Gaussian absolute addressing and more than $2$M for categorical absolute addressing. The experiment does not test learned geometry or repeated retrieval. Instead, it motivates the hypothesis that stateful retrieval may be easier to learn than stateless. To test the full proposed method further experimentation is necessary.
