We propose SyneState: a communication prosthetic built on DeepSeek's Manifold-Constrained Hyper-Connections (mHC) architecture. By parameterizing cross-channel mixing as a doubly-stochastic matrix constrained to the Birkhoff polytope, we can: (1) induce machine synesthesia—stable, tunable cross-modal binding between latent streams; (2) learn personalized binding matrices that approximate individual cognitive architectures; and (3) translate between compression levels—expanding high-compression encodings into explicit single-channel representations and vice versa.
For mHC implementers, SyneState is a direct application of manifold-constrained mixing to cross-modal binding. For cognitive science and clinical researchers, it offers a candidate prosthetic for the double-empathy problem: bridging communication gaps not by "fixing" either party, but by learning the translation between different cognitive compression schemes.
All required components—multi-stream residuals, Sinkhorn projection, multimodal attention heads—exist in production stacks today. This is integration work, not research.
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We propose SyneState: a communication prosthetic built on DeepSeek's Manifold-Constrained Hyper-Connections (mHC) architecture. By parameterizing cross-channel mixing as a doubly-stochastic matrix constrained to the Birkhoff polytope, we can: (1) induce machine synesthesia—stable, tunable cross-modal binding between latent streams; (2) learn personalized binding matrices that approximate individual cognitive architectures; and (3) translate between compression levels—expanding high-compression encodings into explicit single-channel representations and vice versa.