让多个智能体共享的隐空间状态可按内容精准读取
MaSRead: Content-Addressed Reading of Replicated Latent Stores
- 用词元生成密钥标签,按内容路由读取碎片
- 多跳查询可通过词汇连通性图遍历找到所需片段
- 即使新增无关片段也能准确读取,兼容不同模型
独立智能体在隐空间推理时,可将计算状态以键值缓存片段形式共享,而非文本。通过无冲突复制数据类型合并,这些片段在任意传递顺序或重复下均能收敛。但后续未知查询无法可靠读取合并后的缓存:共址片段会相互干扰,导致共址不具可寻址性。MaSRead 解决了内容读取问题,通过从片段词元生成的不透明密钥集进行路由,并在硬注意力掩码下解码每个选中片段,隐藏其余部分。在词汇连通性条件下,图遍历可定位多跳查询所需的片段。在链式、流水线、对称、中心节点及自然语言存储中,MaSRead 能隔离恢复已访问片段,即使积累大量无关片段仍有效,且可迁移至另一模型族。路由后解码仅依赖片段长度而非总存储大小;端到端工作仍包含依赖存储的路由和每访问片段一次读取。局限明确:词汇路由可能遗漏断开证据,答案组合受限于冻结阅读器。因此,当所需片段通过内容与查询相连时,复制的隐空间存储可实现选择性读取。
原文摘要 · Abstract (English)
Independent agents that reason in latent space can share computed state as key-value cache fragments rather than text. Merged by a conflict-free replicated data type, these fragments form a store that converges under any delivery order or duplication. Yet a later query, unknown at encode time, cannot reliably read the merged cache: colocated fragments interfere, so colocation is not addressability. MaSRead addresses the read to content. It routes through opaque keyed tag sets derived from fragment words and decodes each selected fragment under a hard attention mask that hides the rest. Under lexical connectivity, a graph walk reaches the fragments required by a multi-hop query. Across chain, pipeline, symmetric, hub, and natural-language stores, MaSRead recovers visited fragments in isolation, remains effective as unrelated fragments accumulate, and transfers to another model family. After routing, materialized decoding depends on fragment length rather than total store size; end-to-end work still includes store-dependent routing and one read per visited fragment. The limits are explicit: lexical routing can miss disconnected evidence, and answer composition remains bounded by the frozen reader. Thus a replicated latent store becomes selectively readable for later queries when the needed fragments connect to the query through content.
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