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Dual-Channel Tensor Neural Networks: Finite-Sample Theory and Conformal Structure Selection

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arXiv:2605. 19122v1 Announce Type: new Abstract: Tensor-valued data arise naturally in neuroimaging, genomics, climate science, and spatiotemporal networks, where multilinear dependencies across modes carry information that is destroyed under vectorization.

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Authors: Elynn Chen, Jiayu Li, Zheshi Zheng, Jian Pei
Released: 2026-05-20 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Dual-Channel Tensor Neural Networks: Finite-Sample Theory and Conformal Structure Selection overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "#351 follow-up: broader-vocab position-0 sweep at T=1.0 + position-1 suffix isolation". Matching terms: under, line, rate, position, candidate. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.19122

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