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ITNet: A Learnable Integral Transform That Subsumes Convolution, Attention, and Recurrence

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arXiv:2606. 19538v1 Announce Type: new Abstract: Convolutional networks, recurrent networks, and transformers each encode different inductive biases -- locality, sequential memory, and content-dependent pairwise interaction -- and have remained mathematically distinct since their inception.

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Authors: Ashim Dhor, Rasel Mondal, Pin Yu Chen
Released: 2026-06-20 (19d ago)
Topic: Related to current project
Relation to my work: Read next because ITNet: A Learnable Integral Transform That Subsumes Convolution, Attention, and Recurrence overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", 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)". Matching terms: code, under, line, rate, implement, factor, position, model. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.19538

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