Frame Infrastructure for AI, Vol. 1: Beyond Human-Like Memory Toward a Machine-Native Architecture of Structural Persistence

Frame Infrastructure for AI, Vol. 1: Beyond Human-Like Memory Toward a Machine-Native Architecture of Structural Persistence Civilization Physics — Series: Frame Infrastructure for AI This article argues that the dominant memory paradigm for large language models is architecturally misframed. Current systems often treat memory as a context-window problem, then patch the limitation with retrieval, summarization, or larger prompts. These tools are useful, but they do not amount to a stable theory of memory. They reproduce cross-time drift, lossy compression, and hallucination when correction weakens. The central claim is that AI-native memory should not imitate human-like forgetting or pursue perfect token recall. It should preserve source-anchored structure across time. Machine-native memory is therefore defined as governed structural persistence: a system that maintains source fidelity, explicit relational state, provenance, conflict history, and non-destructive compression. The article develops this argument through several linked mechanisms: Memory should be treated as structural persistence rather than temporary context management. A source layer preserves immutable evidence with provenance, versioning, timestamps, validation status, and supersession history. A structural layer maintains stable entities, events, claims, states, task contexts, conflicts, and relations. An index/compression layer accelerates retrieval through embeddings, lexical search, summaries, salience, and caches without replacing the underlying source record. Non-destructive compression prevents summaries from becoming substitute memory and preserves access to rare, contested, or low-frequency evidence. Governed persistence requires explicit update rules for addition, revision, supersession, contradiction, deletion, audit, and calibrated abstention. This article reframes AI memory from a problem of larger context into a problem of cross-time structural integrity. Larger windows, vector databases, RAG, and summarization all help, but none of them alone provide durable memory. A real memory architecture must preserve evidence, model relations, expose conflicts, support correction, and prevent compression from becoming drift. The proposal is implementable with existing components, but it requires a clearer memory doctrine: source first, structure second, compression as access rather than authority. Keywords: AI memory, machine-native memory, structural persistence, frame infrastructure, source fidelity, provenance, non-destructive compression, RAG, vector databases, context-window drift, hallucination, governed persistence, structural memory, AI architecture