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This paper introduces CoDiffGRN, a co-evolutionary discrete diffusion framework for gene regulatory network inference, along with a new benchmark BEELINE-KGC for inductive evaluation. It achieves state-of-the-art performance in novel regulatory discovery.
This paper introduces a novel diffusion-based generative model for structure-based drug design that decouples pocket and ligand representation learning and incorporates multi-scale interaction signals and property-aware optimization to generate developable 3D molecules with improved binding affinity and ADMET properties.
Introduces Generalized Poisson Flow (GPFlow), a variable-length generative framework for protein design that learns an inhomogeneous generalized Poisson process, enabling flexible length exploration and improving designability across structure, sequence, and peptide co-design tasks.
Introduces MobiDiff, an end-to-end discrete diffusion framework for generating human mobility data by denoising multi-channel semantic skeletons, achieving faster inference and competitive fidelity on real-world datasets.
This paper presents CineMobile, a method for efficient on-device image-to-video generation that achieves a 40x speedup over the teacher model through distillation-guided pruning, diffusion distillation, and hybrid quantization, enabling cinematic camera motion effects on mobile devices.
Explores using Google's Gemma diffusion model as a speculative model for efficient large language model inference.
AMD releases Micro-World, an action-controlled interactive world model built on the Wan2.1 family, with open-source weights, code, and a curated dataset for controllable world modeling.
A test of four new frontier AI models (MiMo-V2.5-Pro, MiniMax M3, Mercury 2, LongCat-2.0) using riddles that require genuine reasoning rather than pattern-matching reveals that while most models perform reasonably, LongCat-2.0 repeatedly generates fabricated information with false confidence.
CONFLUX is a 3D latent diffusion model for chest CT synthesis that achieves high-fidelity volumetric generation with controllable clinical attributes, enhanced by a reinforcement learning post-training stage to improve conditioning reliability. The model and a synthetic dataset of ~200k chest CT volumes are released.
This paper introduces CoIn, a novel framework for 3D scene inpainting that bridges 2D diffusion models and 3D Gaussian Splatting via a multi-stage consistency pipeline, enabling both object removal and insertion with flexible masks.
RS-Diffuser 提出了一种风险敏感的离线扩散规划框架，结合扩散轨迹生成和分布值批评器，通过尾部感知目标在推理时灵活调整风险偏好，在安全关键任务中提升了回报和鲁棒性。
This paper introduces A2World, a diffusion-based world model pretrained on large-scale robot manipulation data to learn transferable dynamics priors. The model can be adapted into a real-world simulator (A2World-sim) for policy evaluation or a video-action prediction model (A2World-policy) for action prediction, demonstrating benefits for both simulator-centric and policy-centric robot learning.
PhysisForcing is a training framework that enhances embodied video generation for robotic manipulation by enforcing physical consistency through pixel-level trajectory alignment and semantic-level relational alignment losses in a DiT-based architecture, achieving notable improvements on benchmarks.
This paper proposes a CKM-driven framework for multi-UAV trajectory planning in urban inspection, using diffusion models to reconstruct high-fidelity channel quality maps and a graph attention network with soft actor-critic algorithm for communication-aware path planning.
This technical report presents Qwen-Image-2.0-RL, a post-training pipeline using reinforcement learning from human feedback and on-policy distillation to enhance visual quality and instruction-following in image generation and editing tasks.
This paper introduces Prob-BBDM, a probabilistic Brownian Bridge Diffusion Model for efficient and high-quality MRI sequence synthesis from 2D axial slices, achieving up to 88.46% SSIM and 26.09 dB PSNR with only 4 diffusion steps, and demonstrating clinical utility in tumor segmentation.
NVIDIA announces DFlash, an open source block diffusion model for speculative decoding that achieves up to 15x higher inference throughput on Blackwell GPUs while maintaining interactivity.
This paper presents TryOnCrafter, a novel framework for camera-controllable video virtual try-on that uses a renderable 4D try-on proxy and DiT-based video generation to achieve omnidirectional viewpoint exploration, overcoming the limitations of existing methods that depend on fixed source camera trajectories.
Nvidia claims a 15x speedup in text generation using a diffusion model, generating entire blocks at once.
A diffusion model that can transform any image into an interactive, playable hallucination, running locally on user hardware.