Sovereign Tensor Architecture: A Unified Open Standard for Zero-Copy Storage, Declarative Execution, and Cryptographic Provenance in Distributed AI

Abstract

The rapid proliferation of large neural networks has exposed severe fragmentation in model distribution, execution contracts, and supply-chain verification. Existing weight container formats require multi-file parsing, runtime CPU memory re-alignment, and out-of-band lexical tokenization, introducing significant latency and attack surface for distributed inference. Furthermore, traditional integrity checks rely on fragile out-of-band checksums incapable of defending against weight poisoning or asserting training data provenance. In this paper, we introduce the Sovereign Tensor Architecture—a tripartite open standard comprising: (1) the Sovereign Tensor Container (STC-1.0), a 64-byte aligned binary format for zero-copy Unified Direct Memory Access (DMA); (2) the Sovereign Inference Engine (SIE-1.0), a declarative blueprint schema decoupling runtime backends from neural topologies; and (3) Sovereign Tensor Provenance (STP-1.0), an AI-specific C2PA profile binding cryptographic developer identities, SPDX licensing, and lineage directly into the tensor payload.