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SurF: A Generative Model for Multivariate Irregular Time Series Forecasting

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arXiv:2605. 14069v1 Announce Type: new Abstract: Irregularly sampled multivariate event streams remain a stubbornly difficult modality for generative modeling: tokenization-based approaches break down when inter-event intervals vary by orders of magnitude, and neural temporal point processes are bottlenecked by window-level numerical quadrature.

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Authors: Mohammad R. Rezaei, Tejas Balaji, Rahul G. Krishnan
Released: 2026-05-15 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because SurF: A Generative Model for Multivariate Irregular Time Series Forecasting 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, strong, class, under, token, line, rate, trained. Source: arxiv cs.LG (Machine Learning).
arXiv: 2605.14069

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