为让AI具备持续理解能力，提出构建‘连续性层’新架构。
The Continuity Layer: Why Intelligence Needs an Architecture for What It Carries Forward
- 设计分解-重构式存储机制，实现跨会话的理解延续。
- 在250个故事的ATANT基准上验证了连续性能力的提升。
- 适合关注长期智能系统、记忆架构与伦理治理的研究者。
当前AI最核心的架构问题并非模型规模，而是缺乏一个能传递模型已理解内容的层。会话结束、上下文窗口填满后，内存接口返回的是静态事实，模型需每次重新解读。结果是单次表现强大，但跨时间完全失忆。本文主张，解决此问题的‘连续性层’是领域尚未构建但至关重要的基础设施，其工程实践已公开启动。用于评估该属性的正式框架为ATANT基准（arXiv:2604.06710），在250个故事语料上完成评测；配套论文（arXiv:2604.10981）将其与现有记忆、长上下文及代理记忆基准对比。论文定义连续性为具有七项特性的系统属性，区别于记忆与检索；提出一种存储原语——分解痕迹收敛记忆（Decomposed Trace Convergence Memory），通过写入时分解与读取时重建达成该属性；将工程架构映射至神学中的‘虚己’与符号中的‘阿尔法与欧米茄’，并认为此为结构性对应而非修辞比喻；提出从外部SDK到硬件节点再到长期人类基础设施的四阶段发展路径；分析当前模型层物理限制使连续性层变得尤为关键；主张治理架构（隐私以物理而非政策实现，创始人控制不可协商的架构承诺股份）与产品本身不可分离。
原文摘要 · Abstract (English)
The most important architectural problem in AI is not the size of the model but the absence of a layer that carries forward what the model has come to understand. Sessions end. Context windows fill. Memory APIs return flat facts that the model has to reinterpret from scratch on every read. The result is intelligence that is powerful per session and amnesiac across time. This position paper argues that the layer which fixes this, the continuity layer, is the most consequential piece of infrastructure the field has not yet built, and that the engineering work to build it has begun in public. The formal evaluation framework for the property described here is the ATANT benchmark (arXiv:2604.06710), published separately with evaluation results on a 250-story corpus; a companion paper (arXiv:2604.10981) positions this framework against existing memory, long-context, and agentic-memory benchmarks. The paper defines continuity as a system property with seven required characteristics, distinct from memory and from retrieval; describes a storage primitive (Decomposed Trace Convergence Memory) whose write-time decomposition and read-time reconstruction produce that property; maps the engineering architecture to the theological pattern of kenosis and the symbolic pattern of Alpha and Omega, and argues this mapping is structural rather than metaphorical; proposes a four-layer development arc from external SDK to hardware node to long-horizon human infrastructure; examines why the physics limits now constraining the model layer make the continuity layer newly consequential; and argues that the governance architecture (privacy implemented as physics rather than policy, founder-controlled class shares on non-negotiable architectural commitments) is inseparable from the product itself.
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