{"slug": "balancing-fidelity-and-diversity-in-diffusion-models-via-symmetric-attention", "title": "Balancing Fidelity and Diversity in Diffusion Models via Symmetric Attention Decomposition: Hopfield Perspective", "summary": "Researchers have introduced a method to balance fidelity and diversity in diffusion models by decomposing the attention matrix in transformers into symmetric and skew-symmetric components, interpreting the symmetric part as governing energy landscape structure and the skew-symmetric part as driving circulation. By deriving Hopfield-style stability measures from the symmetric component, the team observed correlations with fidelity-diversity trade-offs in generation and proposed a controllable knob to modulate this balance through circulation modification. The approach, detailed in a new arXiv paper with accompanying code on GitHub, offers a principled way to tune generative model outputs.", "body_md": "arXiv:2605.27476v1 Announce Type: new\nAbstract: We characterize the pre-softmax attention matrix $\\mathbf{QK^\\top}$ in transformers as an associative memory matrix encoding pairwise associations between input features. By decomposing this matrix into its symmetric and skew-symmetric parts, we interpret the symmetric component as governing the structure of the energy landscape, and the skew-symmetric component as driving circulation on that landscape. Leveraging the energy formulation induced by the symmetric component, we derive Hopfield-style stability measures that quantify the stability of retrieved features. We observe meaningful correlations between Hopfield-style stability measures and the fidelity-diversity trade-offs in generation. Finally, we propose a controllable knob to modulate this trade-off by modifying the circulation of the underlying dynamics. Code is available at our GitHub (https://github.com/hyeon-cho/Attention-Symmetric-Decomposition).", "url": "https://wpnews.pro/news/balancing-fidelity-and-diversity-in-diffusion-models-via-symmetric-attention", "canonical_source": "https://arxiv.org/abs/2605.27476", "published_at": "2026-05-28 04:00:00+00:00", "updated_at": "2026-05-28 04:29:07.887496+00:00", "lang": "en", "topics": ["machine-learning", "generative-ai", "neural-networks", "artificial-intelligence", "ai-research"], "entities": ["Hyeon Cho", "GitHub"], "alternates": {"html": "https://wpnews.pro/news/balancing-fidelity-and-diversity-in-diffusion-models-via-symmetric-attention", "markdown": "https://wpnews.pro/news/balancing-fidelity-and-diversity-in-diffusion-models-via-symmetric-attention.md", "text": "https://wpnews.pro/news/balancing-fidelity-and-diversity-in-diffusion-models-via-symmetric-attention.txt", "jsonld": "https://wpnews.pro/news/balancing-fidelity-and-diversity-in-diffusion-models-via-symmetric-attention.jsonld"}}