AI 中文总结
研究针对上下文工程中以令牌计量信息的问题，基于特定机制开发按新颖性分配的缓存作为工作记忆组件，依任务对过去的依赖组织上下文，通过实验验证该方法在不同场景下的有效性及优势，可实现可审计的上下文管理。
AI 中文摘要
上下文工程决定模型传递的信息，当前设计以令牌计量。本文基于一种仅在传入键新颖时才打开缓存槽的机制，开发了按新颖性分配的缓存作为工作记忆组件。根据任务对过去的依赖组织上下文，如召回携带信息存于内容寻址的新颖性缓存等。实验表明，新颖性门控注意力在关注约一半令牌时达到全注意力性能，耦合缓存与状态空间摘要以降低成本匹配全注意力耦合，优势随上下文长度增加，短跨度时滑动窗口更优。保留的记忆是可检查的模板等表格，而非不透明状态。
英文摘要
Context engineering decides what information a model carries forward, and current designs meter it in tokens: compressing the past into a bounded recurrent state, keeping a key-value entry for every token, or imposing a fixed budget through a window or eviction rule. All three make the token the unit of memory even when the stream is redundant and the task depends on the distinct information it carries. Building on a companion mechanism paper that opens a cache slot only when an incoming key is novel, so memory scales with the number of distinct items rather than tokens, we develop that allocate-on-novelty cache as a working-memory component and organize context by how a task depends on the past: recall-carried information belongs in a content-addressed novelty cache, summary-carried information in a recurrent state, and locality-carried information in a recency window. The claim is empirical and bounded. On a matched character-level control, novelty-gated attention reaches full-attention performance while attending to about half the tokens, and coupling the cache with a state-space summary matches full-attention coupling at that reduced cost; the advantage grows as context lengthens, while a sliding window is preferable on short, locality-dominated spans. On next-code prediction over synthetic Medicare claims the coupled component leads full attention and every fixed-budget eviction policy at a thousand-event horizon, whereas cost forecasting over the same stream is summary-carried and the cache is neutral. The retained memory is an inspectable table of templates, codes, drugs, or places rather than an opaque state. The experiments are small-scale and use only public data; they establish the primitive that context can scale with distinct information rather than tokens, in a working memory that is content-addressable and auditable.
Comments16 pages, 1 figure
