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Announcing SimReady Central: An Open Library of Simulation-Ready 3D Assets for Robotics and AI Factories1h◆An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response8h◆Learning Transferable Latent User Preferences for Human-Aligned Decision Making8h◆Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents8h◆IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD8h◆Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value8h◆PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative Models8h◆WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations8h◆When Synthetic Speech Is All You Have: Better Call GRPO8h◆Reducing belief in conspiracy theories as they unfold using large language models8h◆Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors8h◆EDISCO: Equivariant DIScrete Diffusion for Euclidean Combinatorial Optimization8h◆Composing What Each Teacher Learned: Delta Distillation for Multi-Teacher On-Policy Learning8h◆CRISP: Fixing Flying Pixels in Latent LiDAR Generation via Diffusion Decoding8h◆WeGR: Towards Unified Generative Retrieval via Behavior-aware Tokenization and Preference Alignment8h◆StartLux-Decision: AutoThink Decision Models with Reinforcement Learning for Calibrated Deliberative Decisions8h◆Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents8h◆Secure Agentic Coding through Counterexample-Grounded Feedback8h◆Policy Search in Code Space for Multi-Agent Pathfinding8h◆TamedFlow: Trajectory-Aware Distillation for Teacher-Assisted Diffusion Sampling8h◆Announcing SimReady Central: An Open Library of Simulation-Ready 3D Assets for Robotics and AI Factories1h◆An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response8h◆Learning Transferable Latent User Preferences for Human-Aligned Decision Making8h◆Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents8h◆IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD8h◆Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value8h◆PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative Models8h◆WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations8h◆When Synthetic Speech Is All You Have: Better Call GRPO8h◆Reducing belief in conspiracy theories as they unfold using large language models8h◆Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors8h◆EDISCO: Equivariant DIScrete Diffusion for Euclidean Combinatorial Optimization8h◆Composing What Each Teacher Learned: Delta Distillation for Multi-Teacher On-Policy Learning8h◆CRISP: Fixing Flying Pixels in Latent LiDAR Generation via Diffusion Decoding8h◆WeGR: Towards Unified Generative Retrieval via Behavior-aware Tokenization and Preference Alignment8h◆StartLux-Decision: AutoThink Decision Models with Reinforcement Learning for Calibrated Deliberative Decisions8h◆Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents8h◆Secure Agentic Coding through Counterexample-Grounded Feedback8h◆Policy Search in Code Space for Multi-Agent Pathfinding8h◆TamedFlow: Trajectory-Aware Distillation for Teacher-Assisted Diffusion Sampling8h◆

News/model/AnyFlow-Wan2.1-T2V-14B-Diffusers

AnyFlow-Wan2.1-T2V-14B-Diffusers news

1 articles mentioning AnyFlow-Wan2.1-T2V-14B-Diffusers

arxivMay 14

AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation

arXiv:2605.13724v1 Announce Type: cross Abstract: Few-step video generation has been significantly advanced by consistency distillation. However, the performance of consistency-distilled models often degrades as more sampling steps are allocated at test time, limiting their effectiveness for any-ste

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