The Continuity Layer: Why Intelligence Needs an Architecture for What It Carries Forward
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:2604.0...
| Authors | Samuel Sameer Tanguturi |
|---|---|
| Primary category | cs.AI |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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