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arXiv cs.CL

TraceQuery: learning what to recompute from full-denoising trajectory in diffusion language models

arXiv:2610.33008v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) enable flexible generation through iterative masked denoising, but incur substantial inference cost because token representations are repeatedly recomputed across denoising steps. Existing cache-based methods reduce this cost by reusing intermediate results across denoising steps and recomputing only a subset of positions at each step, chosen either on a fixed schedule or by heuristic signals of change. Our trajectory experiments instead point to decoding trajectory as the more relevant quantity. Cached generation remains accurate when it stays close to the full-denoising trajectory, with errors concentrated around unmask-order deviations and reduced as longer reference prefixes are recovered. This sugg

新闻源配图 · arXiv cs.CL
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arXiv cs.CV

Oddin: Latent Visual Reasoning in Unified Multimodal Models via Self-Distillation from Image Generation

arXiv:2610.33062v1 Announce Type: new Abstract: Unified large multimodal models (ULMMs) are able to perform visual reasoning through interleaved text-image generation, but generating explicit intermediate images incurs significant computational overhead. While visual latent reasoning addresses this, most existing methods rely on vision-language models (VLMs) incapable of visual generation. We propose Oddin, a novel framework enabling latent visual reasoning in ULMMs. Oddin replaces intermediate images with compact latent tokens processed entirely by the understanding branch, reserving the generation branch for the final output image. We train the model via offline self-distillation with three objectives, final image generation losses, text distillation losses, and cross-entropy losses for

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arXiv cs.CV

PhaseBreak: Proactive Temporal Protection against Unauthorized Image-to-Video Animation

arXiv:2610.33070v1 Announce Type: new Abstract: Image-to-Video (I2V) diffusion models can animate a single portrait or photograph, enabling shared images to be repurposed for motion synthesis without the subject's authorization. We study proactive visual protection against such use: before an image is released, its owner applies a bounded perturbation that preserves the sharing workflow but disrupts downstream motion generation. Existing spectral disruption objectives globally attenuate temporal representations and therefore provide limited control over subtle or spatially localized motion. Our key idea is that generated dynamics depend not only on temporal feature strength, but also on the order in which feature events occur. We propose PhaseBreak, a white-box spectral retiming protection

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arXiv cs.LG

End-to-End Differentiable Diffusion Conditioning for Physics-Informed Optimization

arXiv:2610.31721v1 Announce Type: new Abstract: Optimizing high-dimensional physical systems under expensive evaluations and complex constraints is a central challenge in science and engineering. In offline model-based optimization (MBO), methods must improve designs using only a fixed dataset and no test-time access to the true objective. However, existing methods typically suffer from conservatism, unconstrained surrogate exploitation, or function as static inference-time samplers that cannot refine candidates against precise physical objectives. In this paper, we propose Differentiable Diffusion Conditioning (D2C) for offline MBO, which leverages the fact that deterministic sampling makes the reverse denoising chain differentiable with respect to the conditioning signal. D2C turns a fro

End-to-End Differentiable Diffusion Conditioning for Physics-Informed Optimization
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arXiv cs.CV

SyncVid: World-Equivariant Diffusion for Synchronized Video Generation

arXiv:2610.31765v1 Announce Type: new Abstract: Generating multiple videos of a dynamic scene from a single reference video requires synthesizing both observed and unobserved content consistently across viewpoints. Existing camera-control methods for video diffusion typically rely on architectural mechanisms, including camera embeddings, cross-view attention, and geometric conditioning. Although effective for static scene content, these approaches often struggle with the fine-grained, dynamic geometry that must remain coherent across views and over time. We synchronize the diffusion process itself by sampling the Gaussian noise prior in a canonical 3D space derived from the source video and rasterizing it into target cameras, rather than sampling independently in each 2D image plane. Our m

SyncVid: World-Equivariant Diffusion for Synchronized Video Generation
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The Verge · AI

Canva has launched its in-house AI image-generation model, Canva Craft, which employs a two-stage design and planning architecture.

Canva has launched its first in-house AI image-generation model, Canva Craft, which employs a two-stage architecture—planning followed by generation—to enhance design accuracy, consistency, and controllability. It supports 2K resolution and over 30 aspect ratios, catering to use cases such as social media, marketing content, and digital art.

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TechCrunch · AI

Canva has launched its first in-house image-generation model, Canva Craft, which is now available to all users.

Canva has officially launched its first in-house image-generation model, Canva Craft, which employs a two-stage generative architecture, supports 2K resolution and output in 30 aspect ratios, and emphasizes text readability and layer editability. The model was trained on millions of human-designed templates within the platform and has already been integrated into Dream Lab and the API; going forward, it will be accessible via MCP to third-party chatbots such as ChatGPT and Gemini.

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