Paper

Diffusion-Driven State Space Models

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arXiv:2606. 21036v1 Announce Type: new Abstract: In many domains, practitioners seek models that produce accurate forecasts while faithfully capturing latent system dynamics.

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Authors: Jack Ruder, Michael Wojnowicz
Released: 2026-06-23 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Diffusion-Driven State Space Models 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, rate, full, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.21036

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