Paper

Sampling Data with Chains of Forward-Backward Diffusion Steps

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arXiv:2605. 27006v1 Announce Type: cross Abstract: Sampling from learned high-dimensional distributions is a foundational computational problem.

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Authors: Hyunmo Kang, Noam Itzhak Levi, Corinna Elena Wegner, Daniel J. Korchinski, Matthieu Wyart
Released: 2026-05-27 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Sampling Data with Chains of Forward-Backward Diffusion Steps 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: strong, rect, under, correct, chain, trained, test, language. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.27006

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