Paper

Covariance-aware sampling for Diffusion Models

Unreadunread

AI summary

arXiv:2605. 13910v1 Announce Type: new Abstract: We present a covariance-aware sampler that improves the quality of pixel-space Diffusion Model (DM) sampling in the few-step regime.

Reading status

Priority

About this paper

Authors: Andrea Schioppa, Tim Salimans
Released: 2026-05-15 (3mo ago)
Topic: Related to current project
Relation to my work: Read next because Covariance-aware sampling for Diffusion Models overlaps with clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)", 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 "Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe". Matching terms: eval, rate, implement, compare, position, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.13910

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Covariance-aware sampling for Diffusion Models. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.