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Few-step generative models produce high-quality samples in one or a few evaluations. However, steering them toward desired properties remains difficult, especially when only black-box reward evaluations are available. While one could apply approaches developed for iterative diffusion and flow-based models, such as dire...
GravityOCR is introduced, a parameter-shared AR-block-diffusion model jointly trained for parallel drafting and causal AR verification, and Verifying drafts before commitment lets the model commit multiple output tokens per round without a separate drafting network.
This work proposes a VLM-driven agentic framework for semantic compositional SVG generation that recursively parses visual scenes into semantic and geometric hierarchies via top-down decomposition, visual grounding, and prompt-driven amodal occlusion recovery, ensuring each component is geometrically complete.
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