Paper

Training-Free Generative Sampling via Moment-Matched Score Smoothing

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arXiv:2605. 14276v1 Announce Type: new Abstract: Diffusion models generate samples by denoising along the score of a perturbed target distribution.

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Authors: Zhenyu Yao, Daniel Paulin
Released: 2026-05-15 (3mo ago)
Topic: Related to current project
Relation to my work: Read next because Training-Free Generative Sampling via Moment-Matched Score Smoothing overlaps with 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", experiment "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: line, rate, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.14276

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