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StAD: Stein Amortized Divergence for Fast Likelihoods with Diffusion and Flow

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arXiv:2605. 16486v1 Announce Type: new Abstract: Diffusion and flow-based models are ubiquitously used for generative modelling and density estimation.

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Authors: Gurjeet Jagwani, Stephen Thorp, Sinan Deger, Hiranya Peiris
Released: 2026-05-19 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because StAD: Stein Amortized Divergence for Fast Likelihoods with Diffusion and Flow overlaps with 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)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: class, under, compare, without, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.16486

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