Cross-Scale Security Alignment Transfer via Full-Weight Tensor Transformation and Capacity Expansion

Manuscript abstract

Abstract

Cosmipher-Freedom-V1.1 is described by its developer as a security-specialized, multimodal mixture-of-experts (MoE) derivative with a reported final parameter total of 602 billion. The development account combines (i) security- and attack-scenario retraining in a supporting model or model family in the 10–30B class, (ii) transfer of learned security alignment to a larger pre-existing MoE backbone, (iii) internal transformations affecting the full set of model weights, and (iv) addition of trainable capacity to attain the claimed final size. We articulate a testable research hypothesis: security-relevant representation structure learned at moderate scale can be aligned across model spaces, propagated through a larger sparse network, and retained after full-weight recalibration and capacity expansion, while preserving general utility. We provide a mathematical research framework for representation alignment, layerwise tensor operators, routing-aware specialization, distribution-preserving regularization, and parameter accounting; reconstruct a complete 38-stage technical pipeline; and specify baselines, ablations, outcome metrics, falsification tests, provenance records, and deployment controls. Importantly, the mathematical mechanisms are proposed formalizations, not a claim that the unpublished proprietary implementation used these exact equations. The developer-reported transformations and 602B total have not been independently verified from full checkpoint inventories, training logs, or benchmark results. Accordingly, this is a hypothesis and methodology paper, not an empirical demonstration of superior security performance or a claim of training the inherited backbone from scratch.

• security language models
• Mixture-of-Experts
• cross-scale alignment
• representation transfer
• full-weight transformation
• model expansion
• red-teaming
• model provenance

Attribution

Cite the manuscript

Cosmipher Research. (2026). Cross-Scale Security Alignment Transfer via Full-Weight Tensor Transformation and Capacity Expansion. Cosmipher Tech Labs. Manuscript.