AI RESEARCH
StrTransformer: Source-Wise Structured Transformers for Unsupervised Blind Source Recovery
arXiv CS.LG
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ArXi:2605.25648v1 Announce Type: cross This paper proposes StrTransformer, a source-wise structured Transformer framework for blind source recovery and branch-wise latent modeling. Instead of using an encoder to infer latent variables, StrTransformer directly optimizes the latent source matrix together with an observation-space mixer and source-wise structural Transformer branches. The mixer enforces reconstruction consistency, while each Transformer branch imposes a differentiable structural constraint on one latent source trajectory.